examples/mel_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from scipy.fftpack import fft, dct, idct, ifft, rfft, irfft\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import python_speech_features as spc\n",
    "import thermocepstrum as tc\n",
    "from scipy.signal import periodogram\n",
    "#from scipy import fft\n",
    "# import scipy._realtransforms as rfft\n",
    "# from scipy import ifft\n",
    "# from scipy.signal.windows import triang\n",
    "from scipy.interpolate import interp1d as interpol\n",
    "# from scipy.special import lambertw, gamma\n",
    "# from scipy import fft.dct, fft.idct\n",
    "#import scipy.fft.dct \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1.0e12\n",
    "def hz2mel(hz):\n",
    "    \"\"\"Convert a value in Hertz to Mels\n",
    "\n",
    "    :param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.\n",
    "    :returns: a value in Mels. If an array was passed in, an identical sized array is returned.\n",
    "    \"\"\"\n",
    "#     return a * np.log(1+hz/a)/np.log(2)\n",
    "    return a / np.log(2) * np.log(1 + np.log(1+hz/a)/np.log(2))\n",
    "\n",
    "\n",
    "def mel2hz(mel):\n",
    "    \"\"\"Convert a value in Mels to Hertz\n",
    "\n",
    "    :param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.\n",
    "    :returns: a value in Hertz. If an array was passed in, an identical sized array is returned.\n",
    "    \"\"\"\n",
    "#     return a*(np.exp(np.log(2)*mel/(a))-1)\n",
    "    return a * (np.exp(np.log(2) * (np.exp(mel*np.log(2)/a) - 1)) - 1)\n",
    "\n",
    "def hz2mel_rec(hz, n=1):\n",
    "    \"\"\"Convert a value in Hertz to Mels\n",
    "\n",
    "    :param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.\n",
    "    :returns: a value in Mels. If an array was passed in, an identical sized array is returned.\n",
    "    \"\"\"\n",
    "    x = np.copy(hz)\n",
    "    for i in range(n):\n",
    "        x = a * np.log(1+x/a)/np.log(2)\n",
    "    return x \n",
    "\n",
    "\n",
    "def mel2hz_rec(mel, n=1):\n",
    "    \"\"\"Convert a value in Mels to Hertz\n",
    "\n",
    "    :param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.\n",
    "    :returns: a value in Hertz. If an array was passed in, an identical sized array is returned.\n",
    "    \"\"\"\n",
    "    x = np.copy(mel)\n",
    "    for i in range(n):\n",
    "        x = a*(np.exp(np.log(2)*x/(a))-1)\n",
    "    return x\n",
    "\n",
    "def get_filterbanks(nfilt=20, nfft=512, samplerate=16000, lowfreq=0, highfreq=None):\n",
    "    \"\"\"Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond\n",
    "    to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)\n",
    "\n",
    "    :param nfilt: the number of filters in the filterbank, default 20.\n",
    "    :param nfft: the FFT size. Default is 512.\n",
    "    :param samplerate: the samplerate of the signal we are working with. Affects mel spacing.\n",
    "    :param lowfreq: lowest band edge of mel filters, default 0 Hz\n",
    "    :param highfreq: highest band edge of mel filters, default samplerate/2\n",
    "    :returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.\n",
    "    \"\"\"\n",
    "    highfreq= highfreq or samplerate/2\n",
    "    assert highfreq <= samplerate/2, \"highfreq is greater than samplerate/2\"\n",
    "\n",
    "    # compute points evenly spaced in mels\n",
    "    lowmel = hz2mel(lowfreq)\n",
    "    highmel = hz2mel(highfreq)\n",
    "    melpoints = np.linspace(lowmel,highmel,nfilt+2)\n",
    "    #print('melpoints=',melpoints)\n",
    "    # our points are in Hz, but we use fft bins, so we have to convert\n",
    "    #  from Hz to fft bin number\n",
    "    bins = np.floor(2*nfft*mel2hz(melpoints)/samplerate)\n",
    "    \n",
    "    fbank = np.zeros([nfilt, nfft])\n",
    "    for j in range(0,nfilt):\n",
    "        \n",
    "        for i in range(int(bins[j]), int(bins[j+1])):\n",
    "            fbank[j,i] = (i - bins[j]) / (bins[j+1]-bins[j])\n",
    "        for i in range(int(bins[j+1]), int(bins[j+2])):\n",
    "            fbank[j,i] = (bins[j+2]-i) / (bins[j+2]-bins[j+1])\n",
    "        fbank[j,:] *= 2./(bins[j+2]-bins[j])    \n",
    "    return fbank, melpoints\n",
    "\n",
    "def mel_filter(arr, nfilt=20, samplerate=16000, lowfreq=0, highfreq=None, axis=0, nrec=1):\n",
    "    \n",
    "    dim = list(arr.shape)\n",
    "    dim[axis] = nfilt+2\n",
    "#     dim[axis] = nfilt\n",
    "    \n",
    "    out = np.zeros(tuple(dim)) #, dtype = np.complex)\n",
    "    \n",
    "    \n",
    "    \n",
    "    highfreq= highfreq or samplerate / 2\n",
    "    assert highfreq <= samplerate / 2, \"highfreq is greater than samplerate/2\"\n",
    "\n",
    "    # compute points evenly spaced in mels\n",
    "    lowmel = hz2mel_rec(lowfreq, nrec)\n",
    "    highmel = hz2mel_rec(highfreq, nrec)\n",
    "    melpoints = np.linspace(lowmel,highmel,nfilt+2)\n",
    "    print('melpoints', melpoints)\n",
    "    nfft = arr.shape[axis]\n",
    "    print('mel2hz={}'.format(mel2hz_rec(melpoints, nrec)))\n",
    "    bins = np.floor(2*nfft*mel2hz_rec(melpoints, nrec)/samplerate)\n",
    "    print('bins=',bins)\n",
    "    for j in range(0, nfilt):\n",
    "        for i in range(int(bins[j]), int(bins[j+1])):\n",
    "            fb = (i - bins[j]) / (bins[j+1]-bins[j])\n",
    "#             out[j] += fb*(1+1j)*arr[i]\n",
    "            out[j+1] += fb*arr[i]\n",
    "            \n",
    "        for i in range(int(bins[j+1]), int(bins[j+2])):\n",
    "            fb = (bins[j+2]-i) / (bins[j+2]-bins[j+1])\n",
    "#             out[j] += fb*(1+1j)*arr[i]\n",
    "            out[j+1] += fb*arr[i]\n",
    "            \n",
    "#         out[j] *= 2./(bins[j+2]-bins[j])\n",
    "        out[j+1] *= 2./(bins[j+2]-bins[j])\n",
    "    out[0] = arr[0]              \n",
    "    out[-1] = arr[int(bins[-1])] \n",
    "\n",
    "    return out, melpoints\n",
    "\n",
    "# def to_filter(flux):\n",
    "def _gamma_inverse(x):\n",
    "    \"\"\"\n",
    "    Inverse the gamma function.\n",
    "    http://mathoverflow.net/questions/12828/inverse-gamma-function\n",
    "    \"\"\"\n",
    "    k=1.461632 # the positive zero of the digamma function, scipy.special.psi\n",
    "#     assert x>=k, 'gamma(x) is strictly increasing for x >= k, k=%1.2f, x=%1.2f' % (k, x)\n",
    "    C=np.sqrt(2*np.pi)/np.e - gamma(k) # approximately 0.036534\n",
    "    L=np.log((x+C)/np.sqrt(2*np.pi))\n",
    "    gamma_inv = 0.5+L/lambertw(L/np.e)\n",
    "    return gamma_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  run_keyword found at line 13372.\n",
      "  column headers found at line 13384. Reading data...\n",
      " #####################################\n",
      "  all_ckeys =  {'Step': array([0]), 'Time': array([1]), 'Temp': array([2]), 'Press': array([3]), 'flux': array([4, 5, 6]), 'Jel': array([7, 8, 9])}\n",
      " #####################################\n",
      "  ckey =  {'flux': array([4, 5, 6]), 'Jel': array([7, 8, 9]), 'Temp': array([2])}\n",
      "    step =    100000 -  49.99% completed\n",
      "    step =    200000 -  99.98% completed\n",
      "  endrun_keyword found.\n",
      "  Retaining an even number of steps (even_NSTEPS=True).\n",
      "  ( 200000 ) steps read.\n",
      "DONE.  Elapsed time:  2.591275930404663 seconds\n"
     ]
    }
   ],
   "source": [
    "data=tc.i_o.read_lammps_log.LAMMPSLogFile(\\\n",
    "                                          'data/log.10.lammps',\n",
    "                                         run_keyword=\"FLUX PRODUCTION RUN\")\n",
    "d=data.read_datalines(select_ckeys=['flux', 'Jel', 'Temp'])\n",
    "vol = 59343.164 # Ang^3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['flux'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using multicomponent code.\n",
      "Using multicomponent code.\n"
     ]
    }
   ],
   "source": [
    "j = tc.HeatCurrent(j=[d['flux'], d['Jel']], DT_FS=5.0, TEMPERATURE=np.mean(d['Temp']),\n",
    "                   units='metal', VOLUME=vol, do_mel=True, mel_nrecursion=1, mel_nfilt=800)\n",
    "jlog = tc.HeatCurrent(j=[d['flux'], d['Jel']], DT_FS=5.0, TEMPERATURE=np.mean(d['Temp']),\n",
    "                   units='metal', VOLUME=vol, do_mel=True, mel_nrecursion=1, mel_nfilt=500,mel_log_flag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  RESAMPLE TIME SERIES\n",
      "-----------------------------------------------------\n",
      " Original Nyquist freq  f_Ny =     100.00000 THz\n",
      " Resampling freq          f* =     100.00000 THz\n",
      " Sampling time         TSKIP =             1 steps\n",
      "                             =         5.000 fs\n",
      " Original  n. of frequencies =        100001\n",
      " Resampled n. of frequencies =        100001\n",
      " PSD      @cutoff  (pre-filter) =    183.53572\n",
      "                  (post-filter) =    183.53572\n",
      " log(PSD) @cutoff  (pre-filter) =      5.21064\n",
      "                  (post-filter) =      5.21064\n",
      " min(PSD)          (pre-filter) =      1.14775\n",
      " min(PSD)         (post-filter) =      1.14775\n",
      " % of original PSD Power f<f* (pre-filter)  = 100.000000\n",
      "-----------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jf=j.resample_current(PSD_FILTER_W=0.1, fstar_THz=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(0.000001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.887260 +/-   0.070688\n",
      "  S_0*   =     3827118.104781 +/- 270531.889641\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.794043 +/-   0.480258  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "-----------------------------------------------------\n",
      "  CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 176  (P* = 177, corr_factor = 1.000000)\n",
      "  L_0*   =          14.300872 +/-   0.047714\n",
      "  S_0*   =     2893819.803599 +/- 138075.459135\n",
      "-----------------------------------------------------\n",
      "  kappa* =           5.137217 +/-   0.245117  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "log mel-filter\n"
     ]
    }
   ],
   "source": [
    "j.mel_cepstral_analysis(debug=True)\n",
    "j.cepstral_analysis()\n",
    "print('log mel-filter')\n",
    "# jlog.mel_cepstral_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07068814764379494"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.242439 / 14.887260 \n",
    "270531.889641 / 3827118.104781"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[270531.88964083 275202.91431548 278307.64741279 279868.30754168\n",
      " 279941.55259345]\n",
      "[0.07068815 0.07196875 0.07296289 0.07367708 0.07412278]\n",
      "138075.4591354817 3.5504749469394664e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.24511672922299083"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(j.mel_psd_std[:5])\n",
    "print(j.mel_psd_std_einsum[:5])\n",
    "print(j.dct.tau_std_Kmin,j.kappa_scale)\n",
    "138075.459135 * j.kappa_scale *0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02556893+3.97991421e-19j 0.00995579-1.14797410e-02j\n",
      "  0.00437377-7.78625829e-03j ... 0.00286917+5.55140351e-03j\n",
      "  0.00437377+7.78625829e-03j 0.00995579+1.14797410e-02j]\n",
      " [0.00995579+1.14797410e-02j 0.02556856-1.73039748e-20j\n",
      "  0.00995553-1.14793874e-02j ... 0.00228912+4.26047535e-03j\n",
      "  0.0028692 +5.55157501e-03j 0.00437381+7.78637833e-03j]\n",
      " [0.00437377+7.78625829e-03j 0.00995553+1.14793874e-02j\n",
      "  0.02556745+8.30590791e-19j ... 0.00200971+3.43809797e-03j\n",
      "  0.00228913+4.26054132e-03j 0.0028692 +5.55157501e-03j]\n",
      " ...\n",
      " [0.00286917-5.55140351e-03j 0.00228912-4.26047535e-03j\n",
      "  0.00200971-3.43809797e-03j ... 0.02556561+1.66118158e-18j\n",
      "  0.00995502-1.14786803e-02j 0.00437364-7.78589816e-03j]\n",
      " [0.00437377-7.78625829e-03j 0.0028692 -5.55157501e-03j\n",
      "  0.00228913-4.26054132e-03j ... 0.00995502+1.14786803e-02j\n",
      "  0.02556745-8.99806691e-19j 0.00995553-1.14793874e-02j]\n",
      " [0.00995579-1.14797410e-02j 0.00437381-7.78637833e-03j\n",
      "  0.0028692 -5.55157501e-03j ... 0.00437364+7.78589816e-03j\n",
      "  0.00995553+1.14793874e-02j 0.02556856-2.24951673e-19j]]\n",
      "[0.06537992 0.05429919 0.03717762 0.02441856 0.02007626 0.02093185\n",
      " 0.02193332 0.02121641 0.0201156  0.01999925 0.02047689 0.0204723\n",
      " 0.01984743 0.01934345 0.01938669 0.01961207 0.01953341 0.01918284\n",
      " 0.01889985 0.01880058 0.01873411 0.01862482 0.01857116 0.01859843\n",
      " 0.01854719 0.01830939 0.01802833 0.01792509 0.01799559 0.01801769\n",
      " 0.01784645 0.01757854 0.01739361 0.01733678 0.01732576 0.01729955\n",
      " 0.01726077 0.01719328 0.01704245 0.01681265 0.01661408 0.01655071\n",
      " 0.01658712 0.01658162 0.01644955 0.01625003 0.01610131 0.01604733\n",
      " 0.01603145 0.01597912 0.01587495 0.01576126 0.01568475 0.01565216\n",
      " 0.01562593 0.01555579 0.01542025 0.01524239 0.01506633 0.01492275\n",
      " 0.01482095 0.01476178 0.01473627 0.01471    0.01463585 0.01450351\n",
      " 0.01436408 0.01428029 0.01425406 0.01422739 0.01415878 0.01407042\n",
      " 0.01400136 0.01393898 0.01383405 0.01368142 0.01354687 0.01349331\n",
      " 0.01350214 0.01349426 0.01341944 0.01329902 0.01318239 0.0130913\n",
      " 0.01301918 0.01295939 0.01290685 0.01284141 0.01274052 0.01261628\n",
      " 0.01251772 0.01247991 0.01248061 0.01246286 0.01239522 0.01229483\n",
      " 0.01219325 0.01209906 0.01200338 0.01190769 0.01182817 0.011773\n",
      " 0.01172886 0.01167564 0.01160844 0.01153985 0.01148376 0.011441\n",
      " 0.01139899 0.01134231 0.01126507 0.01117556 0.01108819 0.01100939\n",
      " 0.01093378 0.01085666 0.01078575 0.01073346 0.01069788 0.01066012\n",
      " 0.01060549 0.01054186 0.01048965 0.010456   0.01042603 0.01037976\n",
      " 0.01031054 0.0102248  0.01013188 0.01004101 0.00996338 0.00990727\n",
      " 0.00986919 0.00983598 0.00979768 0.00975291 0.00969985 0.00963029\n",
      " 0.00954163 0.00945152 0.00938893 0.00936502 0.00935774 0.00933058\n",
      " 0.00926576 0.00917724 0.00909525 0.00904217 0.00901938 0.00900826\n",
      " 0.00898183 0.00892367 0.00884254 0.00876581 0.00871229 0.0086721\n",
      " 0.00861915 0.00854391 0.00846751 0.008419   0.00840296 0.00839487\n",
      " 0.00836701 0.00831301 0.00824698 0.00818529 0.00813396 0.00809018\n",
      " 0.00804913 0.00800741 0.00796346 0.00791794 0.00787344 0.00783222\n",
      " 0.00779351 0.00775285 0.00770404 0.00764362 0.0075763  0.00751579\n",
      " 0.00747592 0.00745657 0.00743931 0.00740178 0.00733888 0.00726853\n",
      " 0.00721486 0.00718606 0.00716938 0.00714548 0.00710575 0.00705657\n",
      " 0.00701042 0.00697385 0.00694242 0.00690619 0.00686102 0.00681437\n",
      " 0.00677747 0.00675059 0.00671967 0.00667196 0.00661379 0.00656657\n",
      " 0.00654238 0.00652772 0.00649707 0.00644142 0.00637824 0.00633175\n",
      " 0.00630764 0.00629017 0.00626125 0.00621768 0.00617019 0.0061306\n",
      " 0.00610217 0.0060793  0.00605284 0.00601608 0.00596899 0.00591847\n",
      " 0.00587383 0.00584078 0.00581897 0.00580359 0.00578794 0.00576517\n",
      " 0.00573105 0.00568739 0.00564187 0.00560226 0.00557021 0.00554127\n",
      " 0.00551032 0.00547554 0.00543775 0.00539863 0.00536133 0.00533052\n",
      " 0.00530869 0.00529196 0.00527174 0.00524196 0.00520427 0.00516593\n",
      " 0.00513352 0.00510875 0.00508866 0.00506797 0.00504216 0.00501033\n",
      " 0.0049757  0.00494216 0.00491042 0.00487881 0.00484806 0.00482249\n",
      " 0.00480429 0.00478805 0.00476421 0.00472875 0.00468752 0.0046501\n",
      " 0.00462089 0.00459749 0.00457569 0.00455337 0.00453002 0.00450576\n",
      " 0.00448191 0.00446045 0.00444078 0.00441844 0.00438938 0.00435519\n",
      " 0.00432201 0.00429394 0.00426967 0.00424626 0.004224   0.00420519\n",
      " 0.00418906 0.00417067 0.00414571 0.00411502 0.00408368 0.00405652\n",
      " 0.00403514 0.00401762 0.00399989 0.00397815 0.00395202 0.00392539\n",
      " 0.00390267 0.00388373 0.00386388 0.00383952 0.00381242 0.00378727\n",
      " 0.0037661  0.00374686 0.00372702 0.00370619 0.00368481 0.00366245\n",
      " 0.00363918 0.0036171  0.00359847 0.00358222 0.00356444 0.00354297\n",
      " 0.00351987 0.00349877 0.00348067 0.00346365 0.00344557 0.00342555\n",
      " 0.0034034  0.00337967 0.00335668 0.0033375  0.00332249 0.00330793\n",
      " 0.00328949 0.00326693 0.00324405 0.00322436 0.00320806 0.00319317\n",
      " 0.00317789 0.00316097 0.00314165 0.00312082 0.00310133 0.00308504\n",
      " 0.00306973 0.00305106 0.00302842 0.00300681 0.00299129 0.00298066\n",
      " 0.00296864 0.00295118 0.0029305  0.00291136 0.00289541 0.00288063\n",
      " 0.0028651  0.00284909 0.00283334 0.00281732 0.00280006 0.00278181\n",
      " 0.00276389 0.00274727 0.00273204 0.00271771 0.00270341 0.00268826\n",
      " 0.00267233 0.00265701 0.00264348 0.00263083 0.00261677 0.00260058\n",
      " 0.00258391 0.00256812 0.0025521  0.0025344  0.00251683 0.00250344\n",
      " 0.0024952  0.0024874  0.00247408 0.00245455 0.002434   0.00241792\n",
      " 0.00240711 0.00239823 0.00238762 0.00237377 0.00235751 0.00234118\n",
      " 0.00232725 0.00231628 0.00230607 0.00229373 0.00227893 0.00226401\n",
      " 0.00225076 0.00223831 0.00222491 0.00221071 0.00219733 0.00218514\n",
      " 0.00217285 0.00215989 0.00214758 0.00213704 0.00212717 0.00211597\n",
      " 0.00210332 0.0020907  0.00207867 0.00206615 0.00205269 0.00203972\n",
      " 0.00202859 0.00201836 0.00200727 0.00199543 0.00198452 0.00197458\n",
      " 0.0019634  0.00194983 0.00193622 0.00192568 0.00191782 0.00190901\n",
      " 0.0018969  0.00188318 0.00187105 0.00186142 0.00185272 0.0018434\n",
      " 0.00183328 0.00182267 0.00181176 0.00180098 0.00179086 0.00178092\n",
      " 0.00176996 0.00175792 0.00174673 0.00173798 0.0017305  0.00172161\n",
      " 0.00171055 0.00169931 0.00168961 0.00168068 0.00167094 0.00166065\n",
      " 0.00165138 0.00164321 0.0016345  0.0016244  0.00161424 0.00160545\n",
      " 0.00159741 0.00158858 0.00157898 0.00156997 0.0015618  0.00155302\n",
      " 0.00154283 0.00153254 0.00152377 0.00151617 0.0015082  0.00149948\n",
      " 0.00149098 0.00148302 0.0014746  0.00146524 0.00145601 0.00144796\n",
      " 0.00144044 0.00143216 0.00142345 0.00141576 0.00140926 0.0014023\n",
      " 0.00139386 0.00138504 0.00137727 0.00137001 0.00136175 0.00135255\n",
      " 0.0013441  0.00133707 0.00133018 0.00132236 0.00131448 0.00130771\n",
      " 0.00130131 0.00129357 0.00128465 0.00127664 0.0012705  0.00126464\n",
      " 0.00125726 0.0012489  0.00124128 0.00123466 0.00122781 0.00122045\n",
      " 0.00121376 0.00120814 0.00120209 0.00119442 0.00118634 0.00117981\n",
      " 0.00117455 0.00116841 0.00116059 0.00115277 0.0011465  0.00114098\n",
      " 0.00113456 0.00112745 0.00112105 0.00111545 0.00110931 0.00110227\n",
      " 0.00109576 0.00109056 0.00108526 0.0010783  0.00107063 0.00106446\n",
      " 0.00106003 0.00105523 0.00104879 0.00104194 0.00103613 0.00103067\n",
      " 0.00102414 0.00101709 0.00101141 0.00100722 0.0010024  0.00099578\n",
      " 0.00098892 0.00098369 0.00097949 0.00097434 0.00096802 0.0009621\n",
      " 0.00095718 0.00095194 0.00094573 0.00093995 0.00093582 0.00093205\n",
      " 0.00092654 0.00091965 0.00091377 0.00090967 0.0009054  0.00089948\n",
      " 0.00089329 0.00088873 0.00088509 0.00088009 0.00087366 0.00086814\n",
      " 0.00086461 0.00086112 0.00085573 0.00084947 0.00084456 0.0008409\n",
      " 0.00083648 0.00083087 0.00082588 0.00082242 0.00081872 0.0008132\n",
      " 0.00080719 0.00080292 0.00079999 0.00079594 0.00079025 0.00078517\n",
      " 0.000782   0.00077888 0.00077371 0.00076766 0.00076336 0.00076075\n",
      " 0.00075714 0.00075152 0.00074613 0.00074284 0.00074016 0.00073577\n",
      " 0.00073031 0.00072628 0.0007238  0.00072038 0.00071502 0.00070997\n",
      " 0.00070703 0.00070456 0.00070004 0.00069428 0.00069023 0.00068824\n",
      " 0.00068543 0.00068031 0.00067512 0.00067222 0.00067021 0.00066625\n",
      " 0.00066076 0.00065675 0.0006549  0.00065241 0.00064756 0.00064254\n",
      " 0.00063984 0.00063811 0.00063427 0.00062869 0.00062472 0.00062333\n",
      " 0.00062141 0.00061677 0.00061165 0.00060906 0.00060783 0.00060443\n",
      " 0.0005988  0.00059455 0.00059322 0.00059171 0.00058735 0.00058218\n",
      " 0.00057961 0.00057881 0.000576   0.00057068 0.00056648 0.00056524\n",
      " 0.00056394 0.00055967 0.00055449 0.00055213 0.00055177 0.00054921\n",
      " 0.00054372 0.00053939 0.0005386  0.00053806 0.0005341  0.00052861\n",
      " 0.000526   0.00052599 0.00052401 0.00051868 0.00051421 0.00051351\n",
      " 0.00051341 0.0005097  0.00050412 0.00050156 0.00050206 0.00050058\n",
      " 0.00049524 0.00049051 0.00049    0.00049042 0.00048688 0.00048096\n",
      " 0.00047831 0.00047943 0.00047864 0.00047329 0.00046819 0.00046787\n",
      " 0.00046912 0.00046606 0.0004598  0.00045676 0.00045824 0.00045816\n",
      " 0.00045297 0.00044758 0.00044741 0.00044935 0.0004466  0.0004399\n",
      " 0.00043657 0.00043868 0.00043947 0.00043428 0.0004283  0.00042818\n",
      " 0.00043101 0.00042883 0.00042174 0.00041807 0.00042087 0.00042263\n",
      " 0.00041737 0.00041056 0.00041044 0.00041438 0.00041292 0.0004053\n",
      " 0.00040108 0.00040462 0.00040758 0.00040227 0.00039448 0.00039437\n",
      " 0.00039976 0.00039926 0.00039088 0.00038574 0.00039007 0.00039462\n",
      " 0.00038941 0.00038035 0.00038011 0.00038724 0.00038798 0.00037866\n",
      " 0.00037231 0.00037765 0.0003843  0.0003792  0.00036838 0.0003678\n",
      " 0.0003772  0.00037965 0.00036909 0.00036101 0.00036765 0.00037724\n",
      " 0.00037247 0.00035922 0.00035807 0.00037058 0.00037551 0.00036318\n",
      " 0.00035244 0.00036075 0.00037469 0.00037059 0.00035375 0.00035147\n",
      " 0.00036846 0.00037735 0.00036267 0.00034785 0.00035842 0.00037897\n",
      " 0.00037618 0.00035391 0.00034963 0.0003735  0.00038888 0.00037067\n",
      " 0.00034908 0.00036306 0.00039472 0.00039464 0.00036329 0.00035516\n",
      " 0.00039083 0.00041829 0.00039458 0.00036062 0.00038022 0.0004328\n",
      " 0.00043906 0.00039092 0.00037455 0.00043323 0.00048654 0.00045339\n",
      " 0.0003935  0.00042417 0.0005238  0.00054776 0.00046289 0.0004261\n",
      " 0.00054146 0.00066537 0.00061366 0.00048433 0.00054572 0.00079251\n",
      " 0.00088757 0.00069261 0.00058532 0.00092354 0.00137216 0.00128944\n",
      " 0.00085049 0.00110959 0.00240486 0.00329761 0.00242353 0.00182841\n",
      " 0.00712403 0.02236005 0.04409034 0.06171473]\n",
      "[0.64493407 0.05863037 0.05374451 0.05374451 0.05374451 0.05374451]\n"
     ]
    }
   ],
   "source": [
    "conv_cc=ifft(fft(j.covxi , axis = 1),axis=0)\n",
    "conv_cc1=fft(ifft(j.covxi , axis = 0),axis=1)\n",
    "print(conv_cc)\n",
    "conv_cc[j.dct.aic_Kmin + 1:,:] = 0.\n",
    "conv_cc[:,j.dct.aic_Kmin + 1:] = 0.\n",
    "print(np.diag(ifft(fft(conv_cc , axis = 0),axis=1).real))\n",
    "print(np.diag(ifft(fft(conv_cc1 , axis = 0),axis=1).real)[:6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x140c955d0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "plt.plot(np.diag(j.covxi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.25488249e-07, 1.27761623e-07, 1.29526457e-07, 1.30794321e-07,\n",
       "       1.31585538e-07, 1.31928826e-07, 1.31860769e-07, 1.31425072e-07,\n",
       "       1.30671612e-07, 1.29655255e-07, 1.28434421e-07, 1.27069424e-07,\n",
       "       1.25620594e-07, 1.24146230e-07, 1.22700482e-07, 1.21331270e-07,\n",
       "       1.20078406e-07, 1.18972074e-07, 1.18031844e-07, 1.17266330e-07,\n",
       "       1.16673566e-07, 1.16242052e-07, 1.15952389e-07, 1.15779310e-07,\n",
       "       1.15693921e-07, 1.15665940e-07, 1.15665773e-07, 1.15666275e-07,\n",
       "       1.15644146e-07, 1.15580907e-07, 1.15463457e-07, 1.15284256e-07,\n",
       "       1.15041142e-07, 1.14736869e-07, 1.14378388e-07, 1.13975950e-07,\n",
       "       1.13542083e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j.mel_psd_std*j.kappa_scale/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAYAAACadoJwAAAHXElEQVR4nO3BMQEAAADCoPVPbQZ/oAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA+A1ALAAFujETzAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f753a50cf10>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.plot(j.mel_psd_std*j.kappa_scale/2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 176  (P* = 177, corr_factor = 1.000000)\n",
      "  L_0*   =          14.300872 +/-   0.047714\n",
      "  S_0*   =     2893819.803590 +/- 138075.459135\n",
      "-----------------------------------------------------\n",
      "  kappa* =           5.137217 +/-   0.245117  W/mK\n",
      "-----------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# jr=j.resample_current(fstar_THz=1.0)[0]\n",
    "j.cepstral_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x142769250>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "conv=j.kappa_scale/2\n",
    "#plt.plot(j.freqs_THz, j.psd*conv, label='no filter', alpha=0.3)\n",
    "plt.plot(jf[0].freqs_THz, jf[0].fpsd*conv, label='wind filter')\n",
    "\n",
    "cesrterr = j.mel_psd_std*conv\n",
    "#plt.plot(j.freqs_THz, j.dct.psd*conv, label='cepstrum')\n",
    "p=plt.plot(j.mel_filtered_freqs_THz, conv*j.mel_dct.psd, label='mel 1k')\n",
    "plt.fill_between(j.mel_filtered_freqs_THz, conv*j.mel_dct.psd-cesrterr , conv*j.mel_dct.psd+cesrterr,alpha=0.9,color=p[0].get_color())\n",
    "#plt.plot(j.mel_filtered_freqs_THz, conv*j.mel_filtered_psd, label='psd mel 1k',alpha=0.6)\n",
    "#plt.plot(jlog.mel_filtered_freqs_THz, conv*jlog.mel_dct.psd, label='logmel 1k')\n",
    "#plt.plot(jlog.mel2hz_rec(jlog.mel_points,jlog.mel_nrecursion)*1e-12, conv*np.exp(jlog.mel_filtered),'o-', label='logmel points')\n",
    "#plt.plot(j.mel_filtered_freqs_THz*1e-24, conv*j10k.mel_dct.psd, label='mel 10k')\n",
    "#plt.plot(j.mel_filtered_freqs_THz*1e-24, conv*j50k.mel_dct.psd, label='mel 50k')\n",
    "\n",
    "\n",
    "# plt.plot(j.mel_filtered_freqs_THz*1e-24, conv*jpsd_sq, label='sq')\n",
    "\n",
    "#plt.plot(jnewfull.freqs_THz,jnewfull.psd*conv, label='media a blocchi') #,marker=marker,ms=ms,lw=lw)\n",
    "#plt.plot(jnewfull.freqs_THz,jnewfull.dct.psd*conv, label='media a blocchi cepstral') #,marker=marker,ms=ms,lw=lw)\n",
    "\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1426a61d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.plot(j.mel_psd_std[:37]/j.mel_dct.psd[:37]- j.mel_psd_std_einsum[:37])\n",
    "#j.mel_psd_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\kappa$ as function of nfilter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NFILTERS =  [ 100  200  300  400  500  600  700  800  900 1000 1100 1200 1300 1400\n",
      " 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800\n",
      " 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200\n",
      " 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500 5600\n",
      " 5700 5800 5900]\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 15  (P* = 16, corr_factor = 1.000000)\n",
      "  L_0*   =          14.637239 +/-   0.176633\n",
      "  S_0*   =     2980501.396116 +/- 526455.852676\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           5.291098 +/-   0.934584  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 17  (P* = 18, corr_factor = 1.000000)\n",
      "  L_0*   =          14.611187 +/-   0.105204\n",
      "  S_0*   =     2903854.556462 +/- 305497.295916\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           5.155031 +/-   0.542330  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 25  (P* = 26, corr_factor = 1.000000)\n",
      "  L_0*   =          14.815882 +/-   0.104277\n",
      "  S_0*   =     3563467.514120 +/- 371586.019444\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.326001 +/-   0.659653  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 25  (P* = 26, corr_factor = 1.000000)\n",
      "  L_0*   =          14.863868 +/-   0.082815\n",
      "  S_0*   =     3738632.616286 +/- 309614.350564\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.636961 +/-   0.549639  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 25  (P* = 26, corr_factor = 1.000000)\n",
      "  L_0*   =          14.878922 +/-   0.070664\n",
      "  S_0*   =     3795341.132885 +/- 268192.786703\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.737632 +/-   0.476106  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.861022 +/-   0.083869\n",
      "  S_0*   =     3728008.234003 +/- 312665.042648\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.618100 +/-   0.555055  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.873753 +/-   0.076147\n",
      "  S_0*   =     3775773.688040 +/- 287514.144358\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.702895 +/-   0.510406  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.887260 +/-   0.070688\n",
      "  S_0*   =     3827118.104781 +/- 270531.889641\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.794043 +/-   0.480258  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.897206 +/-   0.066912\n",
      "  S_0*   =     3865375.107794 +/- 258640.099177\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.861959 +/-   0.459148  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.896801 +/-   0.063793\n",
      "  S_0*   =     3863808.963980 +/- 246482.670794\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.859178 +/-   0.437565  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.901057 +/-   0.061616\n",
      "  S_0*   =     3880286.735424 +/- 239087.504954\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.888430 +/-   0.424437  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 36  (P* = 37, corr_factor = 1.000000)\n",
      "  L_0*   =          14.891468 +/-   0.059588\n",
      "  S_0*   =     3843257.595181 +/- 229012.809423\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           6.822695 +/-   0.406552  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.972577 +/-   0.060325\n",
      "  S_0*   =     4167969.621474 +/- 251431.811314\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.399136 +/-   0.446351  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.967465 +/-   0.059197\n",
      "  S_0*   =     4146719.680520 +/- 245475.305878\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.361412 +/-   0.435777  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.965333 +/-   0.058224\n",
      "  S_0*   =     4137886.996456 +/- 240925.740489\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.345732 +/-   0.427700  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.968589 +/-   0.057073\n",
      "  S_0*   =     4151383.729529 +/- 236931.975496\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.369692 +/-   0.420611  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.971825 +/-   0.056610\n",
      "  S_0*   =     4164836.537470 +/- 235772.291212\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.393574 +/-   0.418552  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.979432 +/-   0.056009\n",
      "  S_0*   =     4196640.621490 +/- 235050.480983\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.450034 +/-   0.417270  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.973706 +/-   0.055495\n",
      "  S_0*   =     4172678.668469 +/- 231562.143919\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.407496 +/-   0.411078  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.975064 +/-   0.055271\n",
      "  S_0*   =     4178348.119799 +/- 230939.968230\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.417560 +/-   0.409973  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.977319 +/-   0.054979\n",
      "  S_0*   =     4187781.400552 +/- 230239.796002\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.434306 +/-   0.408730  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 38  (P* = 39, corr_factor = 1.000000)\n",
      "  L_0*   =          14.992324 +/-   0.054569\n",
      "  S_0*   =     4251092.081408 +/- 231978.175125\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.546698 +/-   0.411816  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.039971 +/-   0.062434\n",
      "  S_0*   =     4458546.615878 +/- 278366.934780\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.914979 +/-   0.494167  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.019900 +/-   0.061758\n",
      "  S_0*   =     4369953.256151 +/- 269879.139463\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.757705 +/-   0.479100  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.028752 +/-   0.061906\n",
      "  S_0*   =     4408808.689617 +/- 272933.700556\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.826682 +/-   0.484522  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.048826 +/-   0.061845\n",
      "  S_0*   =     4498204.126944 +/- 278189.586002\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.985381 +/-   0.493853  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.040202 +/-   0.061698\n",
      "  S_0*   =     4459576.666100 +/- 275146.353269\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.916808 +/-   0.488450  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.037905 +/-   0.061498\n",
      "  S_0*   =     4449346.928551 +/- 273627.410068\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.898647 +/-   0.485754  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.026190 +/-   0.061181\n",
      "  S_0*   =     4397527.470877 +/- 269046.201592\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.806656 +/-   0.477621  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.032933 +/-   0.060746\n",
      "  S_0*   =     4427280.187087 +/- 268938.518696\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.859474 +/-   0.477430  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.040002 +/-   0.060817\n",
      "  S_0*   =     4458685.299154 +/- 271162.713265\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.915225 +/-   0.481378  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.034237 +/-   0.060656\n",
      "  S_0*   =     4433055.584014 +/- 268891.739119\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.869726 +/-   0.477347  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.042945 +/-   0.060936\n",
      "  S_0*   =     4471830.105089 +/- 272494.271589\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.938560 +/-   0.483742  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.022613 +/-   0.061172\n",
      "  S_0*   =     4381825.702376 +/- 268046.386865\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.778781 +/-   0.475846  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.049398 +/-   0.061297\n",
      "  S_0*   =     4500779.476930 +/- 275884.897555\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.989952 +/-   0.489761  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.031117 +/-   0.061311\n",
      "  S_0*   =     4419246.170063 +/- 270947.553120\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.845211 +/-   0.480996  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.029447 +/-   0.061203\n",
      "  S_0*   =     4411871.497369 +/- 270019.207441\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.832120 +/-   0.479348  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.059746 +/-   0.061077\n",
      "  S_0*   =     4547595.591306 +/- 277751.828949\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.073062 +/-   0.493075  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.026202 +/-   0.060932\n",
      "  S_0*   =     4397581.047470 +/- 267953.521518\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.806751 +/-   0.475681  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.029297 +/-   0.060745\n",
      "  S_0*   =     4411213.259198 +/- 267959.262427\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.830951 +/-   0.475691  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.068378 +/-   0.060530\n",
      "  S_0*   =     4587017.925748 +/- 277650.833314\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.143046 +/-   0.492896  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.036577 +/-   0.060338\n",
      "  S_0*   =     4443441.363441 +/- 268106.558753\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.888164 +/-   0.475953  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.007097 +/-   0.060109\n",
      "  S_0*   =     4314362.652916 +/- 259333.364003\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.659018 +/-   0.460378  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.036504 +/-   0.059863\n",
      "  S_0*   =     4443116.772603 +/- 265976.162925\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.887587 +/-   0.472171  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.039196 +/-   0.059605\n",
      "  S_0*   =     4455093.940693 +/- 265546.449366\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.908850 +/-   0.471408  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.072959 +/-   0.059968\n",
      "  S_0*   =     4608081.952756 +/- 276339.692917\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.180440 +/-   0.490569  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.016348 +/-   0.059377\n",
      "  S_0*   =     4354459.659087 +/- 258553.773227\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.730200 +/-   0.458994  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.015048 +/-   0.059018\n",
      "  S_0*   =     4348801.081554 +/- 256655.758838\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.720155 +/-   0.455625  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.059540 +/-   0.060303\n",
      "  S_0*   =     4546656.187413 +/- 274177.629230\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.071394 +/-   0.486730  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.022919 +/-   0.060483\n",
      "  S_0*   =     4383166.978453 +/- 265106.713621\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.781162 +/-   0.470627  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.035509 +/-   0.061609\n",
      "  S_0*   =     4438697.157669 +/- 273462.969132\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.879742 +/-   0.485462  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.031979 +/-   0.061421\n",
      "  S_0*   =     4423058.188755 +/- 271669.249384\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.851979 +/-   0.482277  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          15.047407 +/-   0.061832\n",
      "  S_0*   =     4491823.898479 +/- 277738.446156\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.974054 +/-   0.493052  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.092330 +/-   0.062861\n",
      "  S_0*   =     4698212.683187 +/- 295334.830717\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.340443 +/-   0.524289  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 49  (P* = 50, corr_factor = 1.000000)\n",
      "  L_0*   =          14.986293 +/-   0.062569\n",
      "  S_0*   =     4225532.208831 +/- 264385.458945\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.501323 +/-   0.469347  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.073851 +/-   0.063513\n",
      "  S_0*   =     4612192.089346 +/- 292933.756238\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.187736 +/-   0.520027  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.014854 +/-   0.063763\n",
      "  S_0*   =     4347958.555271 +/- 277238.157295\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.718659 +/-   0.492164  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.048467 +/-   0.063984\n",
      "  S_0*   =     4496587.577759 +/- 287708.938877\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.982511 +/-   0.510752  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.077619 +/-   0.064180\n",
      "  S_0*   =     4629602.229381 +/- 297128.214021\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.218643 +/-   0.527473  W/mK\n",
      "-----------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "jjs = {} # dictionary -> nfilter: j.HeatCurrent\n",
    "NFILTERS = np.arange(100,6000,100)\n",
    "print('NFILTERS = ',NFILTERS)\n",
    "kappa = np.zeros((NFILTERS.shape[0],2))\n",
    "for jfilt in np.arange(NFILTERS.shape[0]):\n",
    "    filt = NFILTERS[jfilt]\n",
    "    jjs[filt] = tc.HeatCurrent(j=[d['flux'], d['Jel']], DT_FS=5.0, TEMPERATURE=np.mean(d['Temp']),\n",
    "                   units='metal', VOLUME=vol, do_mel=True, mel_nrecursion=1, mel_nfilt=filt)\n",
    "    jjs[filt].mel_cepstral_analysis()\n",
    "    kappa[jfilt,0] = jjs[filt].mel_kappa_Kmin\n",
    "    kappa[jfilt,1] = jjs[filt].mel_kappa_Kmin_std\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NFILTERS =  [  100   200   300   400   500   600   700   800   900  1000  1100  1200\n",
      "  1300  1400  1500  1600  1700  1800  1900  2000  2100  2200  2300  2400\n",
      "  2500  2600  2700  2800  2900  3000  3100  3200  3300  3400  3500  3600\n",
      "  3700  3800  3900  4000  4100  4200  4300  4400  4500  4600  4700  4800\n",
      "  4900  5000  5100  5200  5300  5400  5500  5600  5700  5800  5900  6000\n",
      "  6100  6200  6300  6400  6500  6600  6700  6800  6900  7000  7100  7200\n",
      "  7300  7400  7500  7600  7700  7800  7900  8000  8100  8200  8300  8400\n",
      "  8500  8600  8700  8800  8900  9000  9100  9200  9300  9400  9500  9600\n",
      "  9700  9800  9900 10000 10100 10200 10300 10400 10500 10600 10700 10800\n",
      " 10900]\n",
      "[ 6000  6100  6200  6300  6400  6500  6600  6700  6800  6900  7000  7100\n",
      "  7200  7300  7400  7500  7600  7700  7800  7900  8000  8100  8200  8300\n",
      "  8400  8500  8600  8700  8800  8900  9000  9100  9200  9300  9400  9500\n",
      "  9600  9700  9800  9900 10000 10100 10200 10300 10400 10500 10600 10700\n",
      " 10800 10900]\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 50  (P* = 51, corr_factor = 1.000000)\n",
      "  L_0*   =          15.047774 +/-   0.064308\n",
      "  S_0*   =     4493476.296224 +/- 288966.965178\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.976988 +/-   0.512985  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.034595 +/-   0.065805\n",
      "  S_0*   =     4434643.158989 +/- 291822.103751\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.872545 +/-   0.518054  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.084285 +/-   0.065903\n",
      "  S_0*   =     4660568.061490 +/- 307144.993734\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.273615 +/-   0.545255  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.036183 +/-   0.066007\n",
      "  S_0*   =     4441692.998063 +/- 293184.936614\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.885060 +/-   0.520473  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.050753 +/-   0.066079\n",
      "  S_0*   =     4506880.427129 +/- 297809.187756\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.000783 +/-   0.528682  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.040667 +/-   0.066136\n",
      "  S_0*   =     4461654.091348 +/- 295077.993079\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.920496 +/-   0.523834  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.054638 +/-   0.066148\n",
      "  S_0*   =     4524425.945130 +/- 299280.583617\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.031930 +/-   0.531294  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.053111 +/-   0.066162\n",
      "  S_0*   =     4517522.769629 +/- 298888.626518\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.019676 +/-   0.530598  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.054415 +/-   0.066163\n",
      "  S_0*   =     4523416.772280 +/- 299283.021334\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.030139 +/-   0.531298  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.041600 +/-   0.066146\n",
      "  S_0*   =     4465816.996745 +/- 295397.893339\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.927886 +/-   0.524401  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.055374 +/-   0.066007\n",
      "  S_0*   =     4527753.497107 +/- 298863.595239\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.037838 +/-   0.530554  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.068100 +/-   0.065924\n",
      "  S_0*   =     4585742.989024 +/- 302309.055819\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.140783 +/-   0.536670  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.031971 +/-   0.065712\n",
      "  S_0*   =     4423020.311259 +/- 290644.390033\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.851911 +/-   0.515963  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.069120 +/-   0.065472\n",
      "  S_0*   =     4590425.388439 +/- 300544.750865\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.149095 +/-   0.533538  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.018406 +/-   0.065203\n",
      "  S_0*   =     4363427.367567 +/- 284506.400045\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.746120 +/-   0.505066  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.042432 +/-   0.064948\n",
      "  S_0*   =     4469533.836773 +/- 290285.539267\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.934484 +/-   0.515326  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.026546 +/-   0.064690\n",
      "  S_0*   =     4399091.423152 +/- 284578.150841\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.809432 +/-   0.505194  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.036720 +/-   0.064428\n",
      "  S_0*   =     4444075.541276 +/- 286321.826538\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.889289 +/-   0.508289  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.034945 +/-   0.064169\n",
      "  S_0*   =     4436194.979499 +/- 284665.631930\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.875300 +/-   0.505349  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.072685 +/-   0.063904\n",
      "  S_0*   =     4606819.598971 +/- 294393.835195\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.178199 +/-   0.522619  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.039760 +/-   0.063641\n",
      "  S_0*   =     4457608.294395 +/- 283688.799186\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.913313 +/-   0.503615  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.067270 +/-   0.063382\n",
      "  S_0*   =     4581937.890788 +/- 290412.173992\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.134028 +/-   0.515551  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.064333 +/-   0.063126\n",
      "  S_0*   =     4568499.789655 +/- 288388.942458\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.110172 +/-   0.511959  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.044239 +/-   0.062874\n",
      "  S_0*   =     4477616.323624 +/- 281524.598650\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.948832 +/-   0.499773  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 68  (P* = 69, corr_factor = 1.000000)\n",
      "  L_0*   =          15.018269 +/-   0.071638\n",
      "  S_0*   =     4362832.971252 +/- 312545.471000\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.745065 +/-   0.554842  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.052720 +/-   0.062370\n",
      "  S_0*   =     4515754.545763 +/- 281646.540473\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.016537 +/-   0.499989  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.027787 +/-   0.062120\n",
      "  S_0*   =     4404555.835660 +/- 273611.884429\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.819133 +/-   0.485726  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 52  (P* = 53, corr_factor = 1.000000)\n",
      "  L_0*   =          15.054078 +/-   0.061868\n",
      "  S_0*   =     4521891.013903 +/- 279762.574690\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           8.027430 +/-   0.496645  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 98  (P* = 99, corr_factor = 1.000000)\n",
      "  L_0*   =          14.961751 +/-   0.084643\n",
      "  S_0*   =     4123092.687614 +/- 348989.183140\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.319469 +/-   0.619539  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 98  (P* = 99, corr_factor = 1.000000)\n",
      "  L_0*   =          14.968095 +/-   0.084302\n",
      "  S_0*   =     4149331.825380 +/- 349797.003271\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.366049 +/-   0.620973  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 98  (P* = 99, corr_factor = 1.000000)\n",
      "  L_0*   =          14.981633 +/-   0.083955\n",
      "  S_0*   =     4205889.026808 +/- 353107.500399\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.466452 +/-   0.626850  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 98  (P* = 99, corr_factor = 1.000000)\n",
      "  L_0*   =          14.980410 +/-   0.083617\n",
      "  S_0*   =     4200748.352869 +/- 351252.493005\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.457326 +/-   0.623557  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 98  (P* = 99, corr_factor = 1.000000)\n",
      "  L_0*   =          15.001876 +/-   0.083266\n",
      "  S_0*   =     4291896.626687 +/- 357367.855324\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =           7.619136 +/-   0.634413  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/mdsample.py:468: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  out[j+1] = np.sum(arr[int(bins[j]):int(bins[j+2])], axis=axis)/(bins[j+2]-bins[j])\n",
      "//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/cepstral.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  T = 1/Nbins\n",
      "//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/cepstral.py:57: RuntimeWarning: invalid value encountered in multiply\n",
      "  var_diag = Nbins * T**2 * trigamma\n",
      "//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/cepstral.py:64: RuntimeWarning: invalid value encountered in multiply\n",
      "  psd_THEORY_mean = (polygamma(0, N_COMPONENTS) - np.log(N_COMPONENTS)) * Nbins * T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 0  (P* = 1, corr_factor = 1.000000)\n",
      "  L_0*   =                nan +/-        nan\n",
      "  S_0*   =                nan +/-        nan\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =                nan +/-        nan  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 0  (P* = 1, corr_factor = 1.000000)\n",
      "  L_0*   =                nan +/-        nan\n",
      "  S_0*   =                nan +/-        nan\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =                nan +/-        nan  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n",
      "-----------------------------------------------------\n",
      "  MEL CEPSTRAL ANALYSIS\n",
      "-----------------------------------------------------\n",
      "  AIC_Kmin  = 0  (P* = 1, corr_factor = 1.000000)\n",
      "  L_0*   =                nan +/-        nan\n",
      "  S_0*   =                nan +/-        nan\n",
      "-----------------------------------------------------\n",
      "  Mel kappa* =                nan +/-        nan  W/mK\n",
      "-----------------------------------------------------\n",
      "\n",
      "Using multicomponent code.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/cepstral.py:59: RuntimeWarning: invalid value encountered in multiply\n",
      "  var_sdiag [1:-2]=(bins[3:-1] - bins[2:-2])*T[1:-3]*T[2:-2]*trigamma\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-87-3d816d9976dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     jjs[filt] = tc.HeatCurrent(j=[d['flux'], d['Jel']], DT_FS=5.0, TEMPERATURE=np.mean(d['Temp']),\n\u001b[1;32m      7\u001b[0m                    units='metal', VOLUME=vol, do_mel=True, mel_nrecursion=1, mel_nfilt=filt)\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mjjs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_cepstral_analysis\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[0;32m//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/heatcurrent.py\u001b[0m in \u001b[0;36mmel_cepstral_analysis\u001b[0;34m(self, aic_type, Kmin_corrfactor, K_PSD, debug)\u001b[0m\n\u001b[1;32m    230\u001b[0m              \u001b[0;31m#self.mel_psd_std, self.covxi = self.mel_dct.mel_compute_variance(self.mel_var_list,debug)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_psd_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_dct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_compute_variance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_var_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdebug\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    233\u001b[0m         \u001b[0;31m# self.mel_psd_std  is the error on the f=log(kappa), the error on kappa is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \u001b[0;31m# sigma_kappa = d(exp(f(omega)))/d(omega) * self.mel_psd_std = exp(f(omega)) * self.mel_psd_std\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/envs/tcmel/lib/python3.7/site-packages/thermocepstrum/md/cepstral.py\u001b[0m in \u001b[0;36mmel_compute_variance\u001b[0;34m(self, mel_var_list, debug)\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;31m##tmp = irfft(rfft(cov_cc,axis=1),axis=0).real #*cov.shape[0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;31m##tmp = ifft(fft(cov_cc, axis = 1), axis = 0).real #*cov.shape[0] !!!WRONG axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m         \u001b[0mtmp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mifft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcov_cc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m \u001b[0;31m#*cov.shape[0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m         \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdebug\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    332\u001b[0m               \u001b[0;34m(\u001b[0m\u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/envs/tcmel/lib/python3.7/site-packages/scipy/fftpack/basic.py\u001b[0m in \u001b[0;36mfft\u001b[0;34m(x, n, axis, overwrite_x)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \"\"\"\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_pocketfft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite_x\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     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/envs/tcmel/lib/python3.7/site-packages/scipy/fft/_pocketfft/basic.py\u001b[0m in \u001b[0;36mc2c\u001b[0;34m(forward, x, n, axis, norm, overwrite_x, workers)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moverwrite_x\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpfft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc2c\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\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     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "jjs\n",
    "NFILTERS = np.arange(100,11000,100)\n",
    "print('NFILTERS = ',NFILTERS)\n",
    "print(NFILTERS[np.where(NFILTERS==6000)[0][0]:])\n",
    "for filt in NFILTERS[np.where(NFILTERS==6000)[0][0]:]:\n",
    "    jjs[filt] = tc.HeatCurrent(j=[d['flux'], d['Jel']], DT_FS=5.0, TEMPERATURE=np.mean(d['Temp']),\n",
    "                   units='metal', VOLUME=vol, do_mel=True, mel_nrecursion=1, mel_nfilt=filt)\n",
    "    jjs[filt].mel_cepstral_analysis()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "10000",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-90-f2b9c9f6ffff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mjjs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 10000"
     ]
    }
   ],
   "source": [
    "jjs[10000].mel_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'HeatCurrent' object has no attribute 'mel_kappa_Kmin'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-cfabfa978767>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mfilt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNFILTERS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjfilt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mPstar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjfilt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjjs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_dct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maic_Kmin\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----> 6\u001b[0;31m     \u001b[0mkappa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjfilt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjjs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_kappa_Kmin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mkappa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjfilt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjjs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmel_kappa_Kmin_std\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'HeatCurrent' object has no attribute 'mel_kappa_Kmin'"
     ]
    }
   ],
   "source": [
    "Pstar = np.zeros(NFILTERS.shape[0])\n",
    "kappa = np.zeros((NFILTERS.shape[0],2))\n",
    "for jfilt in np.arange(NFILTERS.shape[0]):\n",
    "    filt=NFILTERS[jfilt]\n",
    "    Pstar[jfilt] = jjs[filt].mel_dct.aic_Kmin + 1\n",
    "    kappa[jfilt,0] = jjs[filt].mel_kappa_Kmin\n",
    "    kappa[jfilt,1] = jjs[filt].mel_kappa_Kmin_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.25, 0.981313365688124)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "ls=24\n",
    "fig, ax = plt.subplots(3,figsize=(9,6) ,sharex=True)\n",
    "conv=j.kappa_scale/2\n",
    "ax[0].tick_params(labelsize=20)\n",
    "ax[1].tick_params(labelsize=20)\n",
    "ax[2].tick_params(labelsize=20)\n",
    "#plt.plot(j.freqs_THz, j.psd*conv, label='no filter', alpha=0.3)\n",
    "\n",
    "ax[0].set_ylabel(r'$\\kappa$ ',size=ls)\n",
    "ax[1].set_ylabel(r'$\\sigma(\\kappa)$ ',size=ls)\n",
    "ax[2].set_xlabel('Filter',size=ls)\n",
    "ax[2].set_ylabel(r'P$^{\\star}$',size=ls)\n",
    "\n",
    "ax[0].errorbar(NFILTERS[:], kappa[:,0],kappa[:,1], marker='o',label='wind filter',c='black')\n",
    "ax[1].plot(NFILTERS, kappa[:,1], marker='o',label='wind filter',c='black')\n",
    "ax[1].plot(NFILTERS, kappa[:,1], marker='o',label='wind filter',c='black')\n",
    "ax[2].plot(NFILTERS, Pstar, marker='o',label='wind filter',c='black')\n",
    "\n",
    "ax[0].set_xlim(0,9200)\n",
    "ax[0].set_ylim(4.2,8.9)\n",
    "ax[1].set_ylim(0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prove FFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "covxi = np.random.normal(size=(10,10))\n",
    "covxi = covxi + covxi.T\n",
    "for i in range(len(covxi)):\n",
    "    covxi[i,i] = covxi[i,i]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.18498189e+01, -1.08693999e+00, -1.18234631e-01, -3.67300296e-01,\n",
       "        7.43854604e-03, -8.17742782e-01, -1.84723249e+00,  8.46017361e-01,\n",
       "       -3.98129285e-01,  5.68846951e-01])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covxi[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.28218045,   3.72044044,  -0.13195927,  -1.4529916 ,\n",
       "         -1.55673154,  -0.11714915,  -2.5816563 ,  -0.56051506,\n",
       "         -3.39732923,   1.10498473],\n",
       "       [ -3.63241446,   1.89250474,  -6.15427239,   5.84325628,\n",
       "         -6.49393655,   4.25911506,   2.47828528,  -1.50570898,\n",
       "          5.15370585,  -4.80763151],\n",
       "       [  9.93061302,   4.13819685,  10.28965929,  -5.95984158,\n",
       "          2.01169487,  -5.08043047,  -3.46378546,   5.18636113,\n",
       "         -7.54902944,   4.97533477],\n",
       "       [-12.32641457,  -3.52258707,  -5.80066858,  10.54638873,\n",
       "         -1.65033466,   7.50916763,   4.0336596 ,  -0.67500635,\n",
       "          8.88771886,  -4.82336222],\n",
       "       [  5.97622188,  -0.76901621,   3.70969449,  -0.79742415,\n",
       "          6.02436953,  -1.71750387,  -3.06246027,   3.07055434,\n",
       "         -0.75764012,   9.72540594],\n",
       "       [  2.11257277,   1.54708297,   1.35186574,   0.46265666,\n",
       "          1.34360273,   1.47695638,   2.54253596,   1.81824602,\n",
       "          1.37997884,   3.32531481],\n",
       "       [ -9.33257818,   0.17538205,  -1.62685288,   4.26262915,\n",
       "         -1.79616453,   5.88357037,   7.46115945,   1.35768298,\n",
       "          6.5501317 ,  -5.14861901],\n",
       "       [  8.61237044,   4.15953855,   9.25525405,  -5.53939868,\n",
       "          4.64164212,  -4.68925495,  -4.16636649,   4.36301848,\n",
       "         -7.65446763,   7.72722175],\n",
       "       [ -9.18759826,   0.49836549,  -9.64600796,   8.17804006,\n",
       "         -2.82147908,   8.60740554,   4.34735979,  -0.80317565,\n",
       "          8.61519053,  -6.87377735],\n",
       "       [ 11.20158921,   1.58943543,  -0.4758756 ,  -5.20940897,\n",
       "          2.75829286,  -3.06770545,  -8.32653384,   0.67655634,\n",
       "         -7.74473463,   5.1459079 ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irfft(rfft(covxi, axis = 1), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56217122, -4.23205998, 28.68980229, -3.4367203 , -0.86784575,\n",
       "       -1.26689124,  4.7767631 ,  0.98767477, -0.79032497,  5.71036322])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = ifft(fft(covxi, axis=1), axis=0)\n",
    "tmp[4:, 4:] = 0.0\n",
    "np.diag(ifft(fft(tmp, axis=1), axis=0).real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=fft(np.random.normal(size=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1d4595ed10>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.plot(f.real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'covxi' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-426d91e71b55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcovxi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'covxi' is not defined"
     ]
    }
   ],
   "source": [
    "covxi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "n1,n2=covxi.shape\n",
    "fc = np.zeros(covxi.shape, dtype = np.complex)\n",
    "fcc = np.zeros(covxi.shape, dtype = np.complex)\n",
    "\n",
    "for m in range(n2):\n",
    "    fc[:, m] = np.sum(covxi * np.exp(2*np.pi*1j*m*np.arange(n2)/n2), axis=1)\n",
    "for n in range(n1):\n",
    "    fc[n, :] = np.mean(fc * np.exp(-2*np.pi*1j*n*np.arange(n1)/n1), axis=0)\n",
    "fc[4:, 4:] = 0.0   \n",
    "for j in range(n2):\n",
    "    fcc[:, j] = np.sum(fc * np.exp(2*np.pi*1j*j*np.arange(n2)/n2), axis=1)\n",
    "for j in range(n1):\n",
    "    fcc[j, :] = np.mean(fcc * np.exp(-2*np.pi*1j*j*np.arange(n1)/n1), axis=0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1 = np.arange(n1)\n",
    "r2 = np.arange(n2)\n",
    "rs1 = np.arange(n1)\n",
    "rs1[4:] = 0\n",
    "rs2 = np.arange(n2)\n",
    "rs2[4:] = 0\n",
    "\n",
    "eps = np.exp(2*np.pi*1j*np.outer(r1, rs2)/n1)\n",
    "ems = np.exp(-2*np.pi*1j*np.outer(r1, rs2)/n1)\n",
    "ep  = np.exp(2*np.pi*1j*np.outer(r1, r2)/n1)\n",
    "em  = np.exp(-2*np.pi*1j*np.outer(r1, r2)/n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "cacca=np.einsum('am,bn,jn,mi,ji->ab', eps, ems, ep, em, covxi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13.86518275,  20.96480727,   8.19217098,  24.58919425,\n",
       "        17.17074262, -11.27099686,  86.39999693, 228.78051832,\n",
       "       187.71495494,  50.27952523])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(cacca).real"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}