simmulate_songs-z040.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goes from the input of synthesizer_working (C) to a pickle file \n",
    "## with the template of the model streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-27 13:53:37,791 root         INFO     Computer: passaro\n"
     ]
    }
   ],
   "source": [
    "# simmulate songs from three parameter series (arranges the series too)\n",
    "from __future__ import division\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.signal import spectrogram\n",
    "import scipy.signal as sg\n",
    "import socket\n",
    "import os\n",
    "import sys\n",
    "import logging\n",
    "import h5py\n",
    "import copy\n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "logger = logging.getLogger()\n",
    "handler = logging.StreamHandler()\n",
    "formatter = logging.Formatter(\n",
    "        '%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\n",
    "handler.setFormatter(formatter)\n",
    "logger.addHandler(handler)\n",
    "logger.setLevel(logging.INFO)\n",
    "\n",
    "# Check wich computer to decide where the things are mounted\n",
    "comp_name=socket.gethostname()\n",
    "logger.info('Computer: ' + comp_name)\n",
    "\n",
    "if 'txori' in comp_name or 'passaro' in comp_name or 'lintu' in comp_name:\n",
    "    repos_folder = os.path.abspath('/mnt/cube/earneodo/repos')\n",
    "    experiment_folder = os.path.join('/mnt/cube/earneodo/bci_zf/')\n",
    "\n",
    "if 'lookfar' in comp_name:\n",
    "    repos_folder = os.path.abspath('/Users/zeke/repos/')\n",
    "    experiment_folder = os.path.join('/Users/zeke/bci_zf/')\n",
    "\n",
    "sys.path.append(os.path.join(repos_folder, 'swissknife'))\n",
    "\n",
    "#setup the data folder\n",
    "from swissknife.h5tools import h5tools\n",
    "from swissknife.h5tools import tables\n",
    "from swissknife.dynamo import finch as zf\n",
    "from swissknife.dynamo import takens as tk\n",
    "from swissknife.bci.core import expstruct as es\n",
    "from swissknife.bci import synthetic as syn\n",
    "from swissknife.streamtools import streams as st\n",
    "\n",
    "def plot_spectrogram(x, before_ms, after_ms, s_f, n_window = 192, n_overlap = None, sigma=None, ax=None, **kwargs):\n",
    "    \n",
    "    if n_overlap is None:\n",
    "        n_overlap = n_window/2\n",
    "    if sigma is None:\n",
    "        sigma = 1./1000.*s_f\n",
    "    \n",
    "    # Make the spectrogram\n",
    "    f, t, Sxx = spectrogram(x, s_f, nperseg = n_window, noverlap=n_overlap, window = sg.gaussian(n_window, sigma), \n",
    "                            scaling='spectrum')\n",
    "    \n",
    "    Sxx[[Sxx<np.max((Sxx)*0.000065)]]=1\n",
    "\n",
    "    span_before = np.zeros((Sxx.shape[0], np.int(before_ms/1000. * s_f)))\n",
    "    span_after = np.zeros((Sxx.shape[0], np.int(after_ms/1000. * s_f) + x.size - Sxx.shape[1]))\n",
    "    span_before[:] = np.nan\n",
    "    span_after[:] = np.nan\n",
    "    #Sxx = np.hstack((span_before, (Sxx), span_after))\n",
    "    \n",
    "    if ax is None:\n",
    "        spec_fig = plt.figure()\n",
    "        ax = spec_fig.add_axes([0, 0, 1, 1])\n",
    "        \n",
    "    ax.pcolormesh(((t-0.5*n_window/s_f)*1000.), f, np.log(Sxx), rasterized=True, cmap='inferno')\n",
    "    ax.set_xlim(-before_ms, after_ms + int(x.size/s_f * 1000.))\n",
    "    ax.set_ylim(0,10000)\n",
    "    #ax.plot((span_before.shape[1], span_before.shape[1]), (np.min(f), np.max(f)), 'k--')\n",
    "    \n",
    "    return Sxx, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sys_pars = {'alpha_1': 0.15,\n",
    "            'beta_1': 0.15,\n",
    "            'alpha_2': 0.15,\n",
    "            'beta_2': 0.15,\n",
    "            'gamma': 23500.,\n",
    "            'Ch_inv': 4.5E10,\n",
    "            'Lb_inv': 1.E-4,\n",
    "            'Lg_inv': 1/82.,\n",
    "            'Rb': 5E6,\n",
    "            'Rh': 6E5,\n",
    "            'V_ext': 0.,\n",
    "            'dV_ext': 0.,\n",
    "            'noise': 0.,\n",
    "            'envelope': 0.,\n",
    "            'noise_fraction_beta_1': 0.1,\n",
    "            'noise_fraction_env': 0.1,\n",
    "            's_f': 44100.,\n",
    "            'steps_per_sample': 20\n",
    "            }\n",
    "\n",
    "vocal_pars = {'sys': sys_pars,\n",
    "              'S_1': 0.2,\n",
    "              'S_2': 0.2,\n",
    "              'S_3': 0.2,\n",
    "              'l_1': 1.5,\n",
    "              'l_2': 1.5,\n",
    "              'l_3': 1.0,\n",
    "              'r_out': 0.1,\n",
    "              'r_12': None,\n",
    "              'r_21': None,\n",
    "              'r_23': None,\n",
    "              'r_32': None,\n",
    "              't_12': None,\n",
    "              't_21': None,\n",
    "              't_23': None,\n",
    "              't_32': None,\n",
    "              't_in': 0.5,\n",
    "              'tau_1': None,\n",
    "              'tau_2': None,\n",
    "              'tau_3': None,\n",
    "              'max_tau': None,\n",
    "              'A_1': 0.,\n",
    "              'A_2': None,\n",
    "              'A_3': None}\n",
    "\n",
    "#lines, ab_g= tk.cusp_grid()\n",
    "#lines, ab_g = tk.cusp_grid(b_max=0.075, b_min=-1 / 3, a_step=1.5E-3, b_steps=1000, b_log_step_exp=6, f_max=None)\n",
    "# plt.plot(lines[:,1], lines[:,0])\n",
    "# plt.plot(lines[:,2], lines[:,0])\n",
    "# plt.plot(ab_g[:,0], ab_g[:,1], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load values of alpha, beta\n",
    "bird = 'z040'\n",
    "sess = 'day-2017-10-21'\n",
    "syn_sess = 1\n",
    "\n",
    "fn = es.file_names(bird, '{:03d}'.format(syn_sess))\n",
    "\n",
    "alpha, beta, env = syn.load_alpha_beta(bird, syn_sess=syn_sess, s_f=44100, new_s_f=30000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'folders': {'kai': '/mnt/cube/kai/results/z040/001',\n",
       "  'prb': '/mnt/cube/earneodo/bci_zf/probes',\n",
       "  'proc': '/mnt/cube/earneodo/bci_zf/proc_data/z040/001',\n",
       "  'raw': '/usr/local/experiment/raw_data/z040/001',\n",
       "  'rw': '/mnt/cube/earneodo/bci_zf/raw_data/z040/001',\n",
       "  'ss': '/mnt/cube/earneodo/bci_zf/ss_data/z040/001',\n",
       "  'stim': '/mnt/cube/earneodo/bci_zf/stim_data/z040/001',\n",
       "  'templ': '/mnt/cube/earneodo/bci_zf/templates',\n",
       "  'tmp': '/usr/local/experiment/tmp_data/z040/001'},\n",
       " 'locations': {'archive': '/mnt/cube/earneodo/archive/bci_zf',\n",
       "  'experiment': '/mnt/cube/earneodo/bci_zf',\n",
       "  'experiment_local': '/usr/local/experiment',\n",
       "  'repos': '/mnt/cube/earneodo/repos',\n",
       "  'scratch': '/usr/local/experiment/scratchpad',\n",
       "  'store': '/Data/bci_zf'},\n",
       " 'structure': {'cand': 'experiment.mot.h5',\n",
       "  'evt': 'experiment.kwe',\n",
       "  'kk_par': 'params.prm',\n",
       "  'kk_prb': '*.prb',\n",
       "  'ks_cfg': 'config.m',\n",
       "  'ks_map': 'chanMap.mat',\n",
       "  'ks_mas': 'master.m',\n",
       "  'ks_par': 'params.py',\n",
       "  'mic': 'experiment-rec_000.mic.wav',\n",
       "  'par': 'experiment.par.yml',\n",
       "  'sng': 'experiment.sng.kwe',\n",
       "  'ss_bin': 'experiment.dat',\n",
       "  'ss_lfp': 'experiment.lfp.kwd',\n",
       "  'ss_par': 'experiment.par.yml',\n",
       "  'ss_raw': 'experiment.raw.kwd',\n",
       "  'stm': 'experiment.stm.kwe',\n",
       "  'structure': 'experiment',\n",
       "  'sts': 'experiment-rec_000.sts.wav'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8925ef9d0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sXNJCSYtmHgNXAnvKBmtmZu0YelopIo5Jug64A5gH3BwReyVdm72+RdK5wC7gbOC4pE8A\na4HFwLbs28F84MsR8bVmqpKe1K4TQBrn3VPgZqhXF95Xc+E6Tp0KXXOIiB3AjlnLtvQ9/iG9002z\nPQdcWCXArkrtTEZq8aSizVFlZfbkfqufm7QY/0LazMxynByG8KHoYGO6hXSl0xcp9KUn3vu5Nvpj\nLrRjU5wczMwsx8mhgFTOUaYSR7+y5+xTP9dvI6gyLLm+KE7cri/WVObk0KAUj2hTjGkcUhxJ1mUp\nnLIbxl0+GicHMzPLcXJoSGoHtalNBJiKVs8+lNiZ+61+PuNUjJPDEKkciqYSx4x6Jt4rcZvQCqcv\nUmjDojEkEGrj2umPudCSzXByMDOzHCeHAlI5DE1xBMZ4Jt5rb182XKXmbahv3OXVOTmYmVmOk0OD\nUjjHPVuKMY2Dm6FeXXhfdSDEpDg5mJlZjpNDUxI70Z1YOHNSqS5wvzXAjVqEk8MQqRwup/YL1Dp+\nYVxqC5Um3uuOufAL7lbuIT35zdgYJwczM8txcigglV+pphHFicrGVO2+w6PuK8WWmxxVhlg31Tc+\njVqdk4OZmeU4OcwxqV27GBefi5575sJ1nDo5OZiZWY6TwxBlv2nXfcqz6pee2uOpYxslNlJlv4P2\n1+aUJDO7KvINdmYNnzqvZlBL+3pEMU4OZmaW4+RQQCrfNFKJ4wSlhyu1N8IlyXabIG1Oolh4uz7m\nqszJwczMcgolB0nrJe2TtF/S5gGvXyDpbyT9TNInRylrZmbpGZocJM0DbgSuAtYC10haO2u1Z4CP\nA/+hRNmJltrwucTCGR+3Q61Se58Pkn6EaSly5LAO2B8RByLiKHArsKF/hYg4EhE7gZdGLWtmZukp\nkhyWAY/1PT+ULSuiStkklP1CVPeFtspDWROLp/x+K9xDesB3xzYvW85cJC1ShZl1Urz7X13aONoY\nOHy58b1OhmQuSEvaJGmXpF1PPvnkuMMxM5vTiiSHw8CKvufLs2VFFC4bEVsjYjoipqempgpufm5J\ncXhe2ZjanHjPmlWpL2uLYtZ2/R6prEhy2AmskbRa0gJgI7C94ParlDUzszGZP2yFiDgm6TrgDmAe\ncHNE7JV0bfb6FknnAruAs4Hjkj4BrI2I5waVbaoyZmZWj6HJASAidgA7Zi3b0vf4h/ROGRUqO5dE\npHWI24ERh63w7LT16kRrdiLIdCRzQXrSpHd9ILV40tBm4i6zL/da/SZ5BFidnByGSOXLRmrfdOuK\nZ9ThjHXPyjoOxcJIJNgGtVHDLvw4L1VODgUk800jkTD6lW2aSpO1tbgvG67SbUIbm3jPqnJyMDOz\nHCcHMzPLcXJoWGpnPFO7djEuPhVdry60p9/7o3FyMDOzHCeHITzx3knU9CVs1HrV/Q213Yn3egrd\nQ/qVifeai2fc2jjaGHgP6eZ3OxGcHMzMLMfJoYBUvmmkEke/0reQrlCbUYdOpveDxMlSbeI9j2VN\nlZODmZnlODmYmVmOk0PDUvv5fmLhjI2boV5dGCbq9/5onBzMzCzHyWGocl83Uhs5mlo8ZbdTbeK9\nAfeQbnGs6MyuitRhZp1JvpjextHGwHtIT26T1srJoYBU3kypxNGv7B9XT7w3OSr1ZWODldzpVTk5\nmJlZjpODmZnlODmYmVmOk0PDPHouTakNMe66LjRnF2JMiZPDEMm8oVKJI1NXu4x8m9AKO06lL4vE\nkUqsTWpn4r050JANcXJoSGojZJK51Wli2myWMn3gbqufRzIV4+RQQCof0BTf1KXvIV1ppy3uywpI\n8B7S7vTKnBzMzCynUHKQtF7SPkn7JW0e8LokfS57/UFJF/e9dlDSbkn3S9pVZ/BmZtaM+cNWkDQP\nuBG4AjgE7JS0PSIe6lvtKmBN9u8S4IvZ/zMuj4inaovazMwaVeTIYR2wPyIORMRR4FZgw6x1NgB/\nFD33AOdIOq/mWDsptVEnqcUzLm6GenWhPbsQY0qKJIdlwGN9zw9ly4quE8Bdku6VtKlsoONS9g1V\n9+igqkPy6p94r56PWqsT7w1YNo7rlkXabmbI7iRfV23lj7Un3itt6GmlGlwWEYclLQHulPRwRHx9\n9kpZ4tgEsHLlyhbCMjOzkyly5HAYWNH3fHm2rNA6ETHz/xFgG73TVDkRsTUipiNiempqqlj0LUll\nCGmK33hK30O61VlZE2y4CVKteZvpG/d4dUWSw05gjaTVkhYAG4Hts9bZDnwoG7V0KfBsRDwhaaGk\nRQCSFgJXAntqjN/MzBow9LRSRByTdB1wBzAPuDki9kq6Nnt9C7ADuBrYD7wA/HZWfCmwLfvmNh/4\nckR8rfZamJlZrQpdc4iIHfQSQP+yLX2PA/idAeUOABdWjNHMzFrmX0gPUXX2ztpG9dQ10V1q8bQ4\nXKl/X+Mc0lto4r3mw6hVmfZsZ+K9/v11rVXHy8lhjvA12ZMYwz2kRytUexhmhTg5FJDKH9ZU4uhX\neuK9CpUZtWyCzTZRqrRvcxPvudercnIwM7McJwczM8txcjAzsxwnh4alNkAitXjGxbePrFv67Zl+\nhGlxchgilTdUKnHMqCueUf9IV/mj3qWEMDeSePOVnBvt2Awnh4akNlgitXhS0WazlJmjK5V5vSaJ\nPwvFODkUkMp7Kc0/FO3HNPrEe42EYZk2J1EsvF33eWVODmZmluPkYGZmOU4OZmaW4+Qwx3jwRsYN\nUasujArqQowpcXIYIpU3VGozStYVz6ibqbLbVJqw2KysiQTboHZmZZ38dmyKk0MRJYY+NDGyqNqo\nkIZux1h64r329nmqSdjaHNVSZl9dGHVT5b3V2MR7p3ytA42aACcHMzPLcXIwM7McJwczM8txcjAz\nsxwnhyGqjnWo797PNW2nrlFGtWylxH6rjFY6yeO2FRpB07FBNqXuIV1/GPl9nLCTjjXqmDk5mJlZ\njpNDAaXuC5/aaLkxDBk8dbkKwx9rrEybwxpLvY9qj6J+XRtindxnM1FODmZmllMoOUhaL2mfpP2S\nNg94XZI+l73+oKSLi5Y1M7P0DE0OkuYBNwJXAWuBayStnbXaVcCa7N8m4IsjlDUzs8QUOXJYB+yP\niAMRcRS4Fdgwa50NwB9Fzz3AOZLOK1jWzMwSM7/AOsuAx/qeHwIuKbDOsoJla/Prn/8Gf/fSy7Vu\n8/CPX+TNSxeVLv8PP///OK2GK2CPPvMC61a/rvJ2PnbLfbzq9HmVt/PTnx2rvA2AX/vcaO3z+I9f\nZPlrX11qX39yzw+4ffcTABw7Pr5hjb/++W8MrfMLR+t9Hzdtx54n+PajPxqpzI9fONpQND93/W27\nefWC3vv9JzW9Z+eKIsmhFZI20TslxcqVK0tt4xemFnL05eN1hsWapWdxxdqlI5d771uWsPfx53j5\neD3xrFl6Fv/owjeULv/2Fefwmxcv58WX6vuA/OoZp3PBuWeXKvvetyzhoSdGb581S8/iXW+eGnl/\nv/ueNXzvyPMnLHvb8tfwq2sWj7ytst7zliXsGeE98Su/8HrWvqFc+7Zp0zvPZ+fBZ0Yu9+ali9hw\nUfn39KlccO7ZvH96Bc//7KUTlr/7VQs4f2phI/us6g/efxFLFp0x7jBeoWE/ipL0DuCGiHhf9vx6\ngIj4TN86fwj8n4i4JXu+D3g3sGpY2UGmp6dj165d5WpkZjYHSbo3Iqbr2l6Raw47gTWSVktaAGwE\nts9aZzvwoWzU0qXAsxHxRMGyZmaWmKGnlSLimKTrgDuAecDNEbFX0rXZ61uAHcDVwH7gBeC3T1W2\nkZqYmVlthp5WGgefVjIzG804TiuZmdkc4+RgZmY5Tg5mZpbj5GBmZjlODmZmlpPkaCVJTwI/KFl8\nMfBUjeGM26TVB1ynrpi0Ok1afeDEOr0xIkafPuAkkkwOVUjaVedwrnGbtPqA69QVk1anSasPNFsn\nn1YyM7McJwczM8uZxOSwddwB1GzS6gOuU1dMWp0mrT7QYJ0m7pqDmZlVN4lHDmZmVtHEJAdJ6yXt\nk7Rf0uZxxzOMpIOSdku6X9KubNnrJN0p6XvZ/6/tW//6rG77JL2vb/nfz7azX9LnpBpuO1cs/psl\nHZG0p29ZbfFLOkPSn2XLvylp1ZjqdIOkw1k/3S/p6q7USdIKSX8l6SFJeyX9bra8s/10ijp1uZ/O\nlPQtSQ9kdfo32fLx9lNEdP4fvenAHwHOBxYADwBrxx3XkJgPAotnLfv3wObs8Wbgs9njtVmdzgBW\nZ3Wdl732LeBSQMDtwFUtxf9O4GJgTxPxA/8S2JI93gj82ZjqdAPwyQHrJl8n4Dzg4uzxIuC7Wdyd\n7adT1KnL/STgrOzx6cA3s7jG2k+N/xFp4x/wDuCOvufXA9ePO64hMR8knxz2Aedlj88D9g2qD737\nY7wjW+fhvuXXAH/YYh1WceIf0trin1knezyf3g99NIY6neyPTmfq1BfLV4ArJqGfBtRpIvoJeDXw\nbeCScffTpJxWWgY81vf8ULYsZQHcJele9e6fDbA0enfQA/ghMHPz6pPVb1n2ePbycakz/lfKRMQx\n4Fng9c2EPdTHJD2YnXaaObTvVJ2y0whvp/etdCL6aVadoMP9JGmepPuBI8CdETH2fpqU5NBFl0XE\nRcBVwO9Iemf/i9FL8Z0dStb1+Pt8kd7pyouAJ4D/ON5wRifpLOB/AZ+IiOf6X+tqPw2oU6f7KSJe\nzv4eLAfWSXrrrNdb76dJSQ6HgRV9z5dny5IVEYez/48A24B1wN9KOg8g+/9ItvrJ6nc4ezx7+bjU\nGf8rZSTNB14DPN1Y5CcREX+bfXCPA/+VXj+dEF8myTpJOp3eH9E/jYjbssWd7qdBdep6P82IiB8D\nfwWsZ8z9NCnJYSewRtJqSQvoXXDZPuaYTkrSQkmLZh4DVwJ76MX84Wy1D9M7n0q2fGM24mA1sAb4\nVnbI+ZykS7NRCR/qKzMOdcbfv61/Avxl9u2pVTMfzsxv0Osn6ECdsv3/N+A7EfGf+l7qbD+drE4d\n76cpSedkj19F7xrKw4y7n9q6cNTChZyr6Y1ceAT4/XHHMyTW8+mNNngA2DsTL71zgH8BfA+4C3hd\nX5nfz+q2j74RScA0vQ/CI8B/ob0LZ7fQO3x/id65zY/UGT9wJvA/gf30RmCcP6Y6/TGwG3gw+4Cd\n15U6AZfROxXxIHB/9u/qLvfTKerU5X56G3BfFvse4FPZ8rH2k38hbWZmOZNyWsnMzGrk5GBmZjlO\nDmZmluPkYGZmOU4OZmaW4+RgZmY5Tg5mZpbj5GBmZjn/H97SGVAUC2t2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8c058a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JdTU8duslrN15iP1tnfz+zT38du0u/uWpNwAYO7KKD50zhfmzxjFr/EjOmjom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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8c058a610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c6Pe9sz7ic7W08cVMojOt4qITQyEXmUwZDQAtLcZBv7RT5MUSAC5/+iv8/JUV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\nrZdDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd89212be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alpha)\n",
    "plt.figure()\n",
    "plt.plot(beta)\n",
    "plt.figure()\n",
    "plt.plot(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def latent_to_stream(onof, beta, alpha):\n",
    "    onof[onof>.3] = .3\n",
    "    onof[onof<0] = 0\n",
    "    alpha[alpha<0]=0\n",
    "    return np.vstack([0.15 - onof, -beta, alpha]).T\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BMG/6SGaMruCrFx2fb1E8gRoJRQHmzxzTGb71QyfhszGvIEaylkRsdFMq0yxi\njuuOkOG1bQcxBk6bOCwjWQBaraGsOw4e4wsPvElTW4iPnD4+7fS8zNCyIpZ/bR7Hj7bfWhwIaHeT\nogCnTBjGU189jwmVZY4NPSxN2pKIGolUWhKxj/v9r2znrZ1HGFZWxKkT0jcSPp9Yc2bC/H7lTm56\ndB3FAR/fuexEzpmWr/USFS+gRkJRLJwevVYS8NERihCJmG4tk3A6jmtrpM1r2w4x7/iR3HbFLCpK\nU3dYxzOoyE9rMMydz27mlAnD+PlVpxXESDklu2h3k6JkiZhjNBjp3pqI+SRSMRLxcyDu/cxsWx/1\n0iI/m/cdZU9jGx+bPV4NhJIS2pJQlCwRMwI9bESnTyIVt0d5SYDtP4juwJnpWlExBhX5eWvXYQBO\ncmCIrzIwUCOhKFkiNjE/1r0UIzYENtUF5ewahxilRX7agtGFJmeo01ZJEe1uUpQsEWtJxIxCjJjj\n2okRVOkQG+Y6abhzznml8FEjoShZIrbwXVIj4VALIVViM4zdsLyM4h3USChKlvAnNRLW+RwbiZjR\nOsGB2eTKwEGNhKJkiVh3UiSJTyLHNoLtB44BZDRjWxm4qJFQlCzhT+KTMGlMpnOSD58WnVl92kQ1\nEkrqqPdKUbJEsu6m2NYQufZJ3PD+qVw1dwLl6rRW0kBbEoqSJZL7JFKfJ+EkAb8vpX28FSUeNRKK\nkiViRmJPYxv/9vBq9jVF95aIGIP0s8WmorgFNRKKkiVi3Um/+sd2Hn2rnr9Ze2FHjMn5yCZFyRQ1\nEoqSJWJDTrfuPwpAQ3O0JRGO5N4foSiZokZCUbJEbAhsbOjpgeYOIDq6yae/PMUj2BrmICLDgYeB\naqAO+IQx5nCC6+qAZiAMhIwxNXbyVRQv0LNL6Vh7CIg6srUloXgFu/WZxcCzxpjpwLPWcTLeb4w5\nVQ2EMlDoOQ+iLRTdFS6sPgnFQ9g1EpcDv7HCvwGusJmeohQMPRfwi21lakzuF/dTlEyxayRGG2P2\nWOG9wOgk1xlghYisEpFFNvNUFE8Q6GEIYluZRrub8iGRoqRPvz4JEVkBjElw6ub4A2OMERGT4DqA\nc40x9SIyCnhGRN4xxryYJL9FwCKAiRMn9ieeoriWnn6HtmDUSESMyfmSHIqSKf0aCWPMRcnOicg+\nERlrjNkjImOBhiRp1Fv/G0TkMWAOkNBIGGPuBe4FqKmpSWZ0FMX19DQE7VZ3U3QynRoJxRvY7W56\nArjGCl9hbTlVAAAffUlEQVQDPN7zAhEZLCIVsTAwH1hvM19FcT3+Hr+umE8iEsn9MuGKkil2jcQd\nwMUishm4yDpGRI4TkWXWNaOBf4jIGmAlsNQY85TNfBXF9fh7TIbosFb2Cxv1SSjewdY8CWPMQeDC\nBPHvAQut8DbgFDv5KIoXSea4jhijo5sUz6DzPhUlS8T7JMpLAl1GQifTKR5CjYSiZIlADyPREYpg\njCFicr/hkKJkihoJRckS8YZgcIkfiPolwtZS4YriBdRIKEqWKIob3lReWgRE/RJGl+VQPIQaCUXJ\nEvEtiQpry9COUEQX+FM8hRoJRckSPX0SEG1JhCO6dpPiHdRIKEqW6Da6qbSrJWGM6TXRTlHcir6q\nipIlAnGT6bpaEmFrMp22JBRvoEZCUbKE39+7u0l9EorXUCOhKFki0G0IbJdPQleBVbyEGglFyRKB\nJD6JcESHwCrewdbaTYqiJCfRENj2UJjXth3Kl0iKkjbaklCULBG/Z0S8T0JRvIQaCUXJAbHupsMt\nQQA+Pnt8PsVRlJRRI6EoOSDWkti0txmA958wKp/iKErKqJFQlBwQMxIb9zQBMGXk4HyKoygpo0ZC\nUXJAbAjshvpG/D6heoQaCcUbqJFQlBwQa0kc6whTPaKM0iJ/niVSlNRQI6EoOaC0qOundsKYIXmU\nRFHSw5aREJGPi8gGEYmISE0f1y0QkU0iskVEFtvJU1G8SPxw2ONHV+RREkVJD7stifXAR4AXk10g\nIn7gF8ClwEzgKhGZaTNfRfEsM8aokVC8g60Z18aYt6F7LSkBc4Atxpht1rV/AC4HNtrJW1G8yglq\nJBQPkQufxDhgV9zxbitOUQYkE4aX5VsERUmZflsSIrICGJPg1M3GmMedFkhEFgGLACZOnOh08oqS\nd3QFWMVL9GskjDEX2cyjHpgQdzzeikuW373AvQA1NTXGZt6K4hp+eXUNFaW6pqbiLXLxxr4BTBeR\nyUSNw5XAp3KQr6LknRs/MI2widZ1Lp45Os/SKEr62DISIvJh4H+AkcBSEVltjLlERI4D/s8Ys9AY\nExKRLwHLAT9wnzFmg23JFcUD/Nv8GfkWQVFsYXd002PAYwni3wMWxh0vA5bZyUtRFEXJPWKMe7v9\nRWQ/sCPD26uAAw6Kk28KTR9QnbxCoelUaPpAd50mGWNGOpWwq42EHUSk1hiTdBa41yg0fUB18gqF\nplOh6QPZ1UnXblIURVGSokZCURRFSUohG4l78y2AwxSaPqA6eYVC06nQ9IEs6lSwPglFURTFPoXc\nklAURVFsUnBGwmt7V4hInYisE5HVIlJrxQ0XkWdEZLP1vzLu+pss3TaJyCVx8bOtdLaIyJ3Sz9K8\nDsp/n4g0iMj6uDjH5BeREhF52Ip/XUSq86TTEhGpt8pptYgsjDvnap1EZIKIPC8iG639X75ixXu2\nnPrQycvlVCoiK0VkjaXTrVZ8fsvJGFMwf0RndG8FpgDFwBpgZr7l6kfmOqCqR9yPgMVWeDHwQys8\n09KpBJhs6eq3zq0EzgQEeBK4NEfyzwNOB9ZnQ37gi8DdVvhK4OE86bQE+HqCa12vEzAWON0KVwDv\nWnJ7tpz60MnL5SRAuRUuAl635MprOWX9I5LLP+AsYHnc8U3ATfmWqx+Z6+htJDYBY63wWGBTIn2I\nLnVylnXNO3HxVwH35FCHarp/UB2TP3aNFQ4QnTAkedAp2cfHMzrFyfI4cHEhlFMCnQqinIAy4E1g\nbr7LqdC6m7y4d4UBVojIKokukw4w2hizxwrvBWIrwyXTb5wV7hmfL5yUv/MeY0wIaARGZEfsfvmy\niKy1uqNiTX5P6WR1L5xGtJZaEOXUQyfwcDmJiF9EVgMNwDPGmLyXU6EZCS9yrjHmVKLbu94gIvPi\nT5qoyffsEDSvyx/HXUS7MU8F9gA/zq846SMi5cAjwFeNMU3x57xaTgl08nQ5GWPC1vdgPDBHRGb1\nOJ/zcio0I5HW3hVuwBhTb/1vILpY4hxgn4iMBbD+N1iXJ9Ov3gr3jM8XTsrfeY+IBIChwMGsSZ4E\nY8w+6wccAX5JtJy6yWfhSp1EpIjox/RBY8yjVrSnyymRTl4vpxjGmCPA88AC8lxOjs+TEJH7gH8C\nGowxs6y44cDDRPt564BPGGMO95dWVVWVqa6udlQ+RVGUQmbVqlUHgO3AjUS74JYB/2OMWSYiNwDv\nM8ZcLyJXAh8xxnyir/SyYSTmAUeB38YZiR8Bh4wxd0h0WGqlMeZb/aVVU1NjamtrHZVPURSlkBGR\nVcD1wP3AIKKjm75sjDEiUgr8jqgP5xBwpTFmW1/pOb4znTHmxQRjby8HLrDCvwFeAPo1EoqiKPmk\noamNObc/C8CL33g/E0eU5Vmi1DDG1AKzEsS3AR9PJ61c+SSSeed7ISKLRKRWRGr379+fG+kURVES\nsK6+sTN8+7K38yhJ/si547o/77wx5l5jTI0xpmbkSMf2zVAURUmbkoC/M9zcHsyjJPkjV0YimXde\nURTFlQTDEb6/dGPn8ctbcj6ozhXkykg8AVxjha8hOjtSURTFtTz7dgPv7G3Otxh5x3EjISK/B14F\nZojIbhG5DrgDuFhENgMXWceKoiiuxemRn14lG6Obrkpy6kKn81IURckWuVlH2f0U2oxrRVEU2zS2\nBPnmn9fmWwxX4HhLQlEUxeuc8r2n8y2Ca9CWhKIoipIUNRKKoihKUtRIKIqiKElRI6EoiqIkRY2E\nogwQHl9dz86DLfkWw9MMxLkTaiQUZYDwlT+s5rI7X8q3GJ6mobk93yLkHDUSijIACEeiNeDm9lCe\nJfE2wXAk3yLkHDUSijIAGIgfNzsMH1ycMD4U1u4mRVEKEDUSqfPiu/s5dKwj4bmXNg+8PW7USCjK\nAGAg1oAzYckTG7j6vpW94s+ZNgKAx1e/l2uR8o4aCUUZAAQj2pJIhftfqUsYP6gouvlQS0c4h9K4\nAzUSijIACGpLwhaxHeoGYredGglFGQCEBuDHLV1W7TiU9NywsiIAAv6B98kceBorygAkviVxzX0r\naWhuy6M07uSjd72a9Nz1508F4JM143MljmtQI6EoA4D4bpK/v7ufj/zvK3mUxls8+sWzKSuOdjf5\nfANvJyLdT0JRBgA9RzcNKS3KkyTupdjvoyPOmH74tHF894MzGVZWTGNLEBiYo8S0JaEoA4COHj6J\nOZOH50kS99LzGT32Vj3DyqKT6vz+aAsiNnN9IJFTIyEidSKyTkRWi0htLvNWlIFMT8f1vib1SfTk\nY7OT+xv81obX4QG4wF8+upveb4w5kId8FWXAEupRA35y/d48SeJepo0qT3rO7+vdknhq/V4mDi9j\n5nFDsi5bPlGfhKIMAHp2pSi96WuYcMAyEvE+iesfWAVA3R2XZVewPJNrn4QBVojIKhFZlOO8FWXA\nMhAdrunS14RDn08QgbA1c/3JdXtyJVbeybWRONcYcypwKXCDiMzreYGILBKRWhGp3b9/4C2mpSjZ\nQCfT9U+ox9IlHzltXLdjY+Av1tpNX3jwzZzJlW9yaiSMMfXW/wbgMWBOgmvuNcbUGGNqRo4cmUvx\nFKVgSdTdVH+kNQ+SuJf41tYPP/o+fvLJU3tds/NQC42twVyKlXdy5pMQkcGAzxjTbIXnA9/LVf6K\nMpBJ1N307t5mxg0blAdp3MnLWw8wqMjPxu9dgkjySXOn3Pp0t2NjTJ/Xe51cOq5HA49ZDzMAPGSM\neSqH+SvKgKVnVwoMzNnDyaitO8T6+iaAtD/4ze2hgp6cmDMjYYzZBpySq/wURemiI0FLwl/Atd90\nOXA0872rg6HC9vfojGtFGQAkclyrjeji+gf6d0RfeMKohPFtaiQURfE6iXwS//x/r+dBEu+SrLXR\nHizsjYjUSCjKAEAn09ln/kljuh3feOF0ANq1JaEoipdZu/sI/7l8U77FcDU1kyoB+Pr845Ne4+/h\n6D9twjBAjYSiKB7nwdd25lsE1zNmaClTqgbzpQ9MT3rNp8+c1O24OBD9fK7dfSSrsuUbNRKKUuC8\n8G5DvkVwPX9bu4dtB471eU15SffBoH+q3QXALY9v6IzbebCFN3cedl7APKJGQlEKnEtnjU16rqUj\nlENJnCUUjtAeyq3T+Mcf7xrFf/0FUzvD19y3kkPHOpj3n8/zkf99BVNAS4rrKrCKUuD0rAHH86WH\n3uK+z56RQ2mcY9rNTwLwwtcvoLpqcE7y/Ojs8VSUBogYOGFM1xLh0S1hX+48vvvv2/hCnBHxMtqS\nUAqOTXubueuFrRnff6w9xK5DLQ5KlF/a+hii+dw73uuKikQMn/tN155lF/zXC1QvXkr14qVs3tec\n9fznnzSGBbPG9IqvO9j1zjzy5u6E99774lY+dtcr7D7snfdLjYRScFzysxf54VPvZNwVcdJ3l3Pe\nj54vmJVTE42+OWX80DxI4gxf/v1brHh7X8JzF//0RZ5Nci4ZsYUOP9xj1Vc7HD7W0SvuB0++ze3L\n3qF2x2HO/eHz/PLFbazcfsixPLOFGgmlYGntSM9IVC9eyqLfdtVQV+8qjFEr8S2JmWOHcOuHTuI/\nrpgFwPhKZxb429/cnpP9n40xLO1nL4frfpPezsixVsljb9VnLFdPDlpGYtPeZuqPtNLYEuSev2/r\nds1ty97mE/e8yqEEBsVNqJFQCoIN7zXSFgzzq39s74x7PY1a2hrLIDy9sasW+rG7X3VOwDzSEmck\nln3lPK45u5qTxw/jnGkjGFVRYjv9Iy0dnHHbCu548m3bacVz4Gg7jS3Bzpr+T57exJzbn03p3nQc\nx2/viS7sN6Q0fRft0hvPTXruE/e8yiU/e5Fz7niOU773dNLrfvnStqTn3IA6rhXPs7exjcvu/Eev\n+M//blXKW0v6Cngho1iL6vrzuztSi/w+Xt55MO30Yt1wAX+0jvn0hqhhffD1ndx82Uw7onaj5vsr\nMr73k/e+xh8/f1a/1x2MW2pjxb+fn3Y+Jx03lC23Xcr3l77N/a/UdTuXrCvpI6eN49G4VstdL2zl\nWwtOSDvvXKFGQvE8H7/nFdtpeMmRmC4tHSHOqK5k8aXdP0QvbIru/NjQ3MaoitJ+0znWHuLbj63j\ncWt3NoBxwwZ11vRb0uzeyyYrtx9KaZ+Hq375Wmd4ZHlmraqA38foIV3Pb9KIMnYcTPw+rbllPkPL\nivjWpSdQEvBx6veeySjPXKLdTYrn2XXI/g5ra+sbux13bXwf4dE3d/O3te/x0mZvbqfb2hFmUHHv\n+uBnz64Gov3mqfDTZ97tZiCg9+526fqBssmqHf1Pant339HOsJ2Ng2Ye1zUc9oMnH5fwmqvPmsTQ\nsui+E6OHlDKsrLjznJsrKWokFM9TWWZ/w5eeaZw1dQQAP39+C//2xzV86aG3+MyvVlK9eCkPvr6D\nje812c4zV7R0hCkr8veK/8xZ0WUm9jX1v5fCfy5/h/+L8/cko6G5LX0BE7C+h9FOxkUnjgbgrCkj\nWDRvSrdzqfiULjs5+UTDdDj/+JH8+fqz+M5lJzJ3yvCE1yz54ElJ77/z2c2OyJENtLtJ8TyHW6J7\nDv/TyWP529ruI1/CEdNrYbZE9Pzof/GCaby0+QA/W9H7x3vzY+s7w6n6PH73ah2zJw3vVuPMFS0d\nYcqKexuJ8ZWDEKHfOSEvvrufXzyf2ryTNbsbmTTC/sS2e17s35n79vcWMKiHXh88+Tg++PMu/9Sh\nYx0MH1zc89ZOlq7te6RUOtRUD6emOmoghg4q6rYX9l9uOCfhToCPfOFsPnrXK/yxdjc/+pg792RT\nI6FkFWMMJ/y/p3qN1U/149of8XMZfv6p0wlHVvHk+r2cPnEYb+48wgOv7eAaq1slGS9vOcBf4rpR\nFs2bwtSRqX3oQuFIpwM3GfVHWvl/1vo+E4eX8bcbz83pdpdtwXCvjylAScDPmCGl7Oqnq+Pq+1b2\nm8ePPnoy33xkLTf+/i0+dEri7pZU+VPtLv665r0+r3nnPxZQmqB19L7xQ/nGJTM6V73dvK+ZuVNG\n9JvnuiXzMxM2CWu+O5+n1u+hyO9jxpgKxleWJbzu9InDOsOpVmhyjXY32eDwsQ7W7W6kevFS16yB\nE44YWjpCrNpxKK8yNTS3sb+5nck3LUs4mat68VKOttuXL7YpffWI6I/wrk/Ppu6Oyzh3+kgAvvvE\nhn6HQ74Z13f92bOrueGCaVSl6MT81iPrCIUjPL1hb8J8GpraOOeO5zqPdx5q4eQlT2fUd//bV+uo\nXrw07fuStSQARlaUdNO/Pz5y2jhuXngii+ZNYdP3F1B3x2XU3XEZCx3qtnly3R6+8ee13eKK/MJn\nzpzEI184uzO/RAYixg3vn9YZfqIfYwNR53tFFoz2glljufDE0UkNBET9IIMsXW55fH3S6/KJtiQy\n5Ncvb+fWv27sPJ55y/LO8OM3nMMpE4Ylui0rtIfC/Psf1/Tqaolx/7VncMGMxFsvZoOWjhBzbut/\nPPus7y5n2+0LEzbD+yMcMfyxdhfHrI/tyeO7P++vXTS9s5938k3LWHbjeUm7elrj5hEs+VBXv/HZ\nU0fwytaD/NvFx3PjhdNpaGpDRDjjtq6hmY+8ubvbEgxPffW8zjV9th84xvv/64WEeV70k7/z8uIP\npKFx12qj1YuX8otPnZ5Sf3okYmgNhilL4LgGWLs72vf/+Op6Lj+1a8bx9/66kfte3s7NC0/sdv26\n+kZ+8slTe6VTXhLgaxcdz09XvGs5ypN/xBPR8/cU45qzJnH12dVMHVmeVnrrb72EWd9dzoOv7+S2\nD78v4TWxStTlp9pr+djlLzecwyU/e7FPWfNJTlsSIrJARDaJyBYRWZzLvJ2itu4QD76+I+ELHePy\nX7zcuZbMv/62lo4sbkqydvcRPnH3q0kNBMBnf/0G1YuXMvf2FWkvWZAuHaEI30vwbH55dQ1nVFf2\nci5O+fYyljyxodf1/TH128u46dF1ncc/+Ej3H5eI8Lvr5nQeL7zzpaRrGO041MKkEWW9usAeuG4u\nT39tHl/+QLRmOmpIKSMrSlj1nYvY9P0FCdNa8LOXOmv7ty3t/RymWAvR1R9p5R+bD/SnJsYYNu1t\n7tVKueGhN/nMr6Lbj97y+HqqFy9lza4jzLzlqW4tyNjkwkiS1tTnzp0MwFf+sLozj3//4xruezl6\n323LuibIDSkN8OC/zk0qa8AfNfZv1KW31MQrWw4k/D3V3XEZt14+K20DAd0XNXx9W+K5ILF5DQ+t\nzO9+GzPGVHSGczFrPV0kV0vaiogfeBe4GNgNvAFcZYxJ+rWtqakxtbXpTbGH6EtRWuR3tDZ/3f1v\nsHFPE3sae4/e+PbCE7h92Tt93r/+1kv6XI0zXZragpy8JPEszhGDi2luCyXdsvKqORN7fVTt0BYM\n8/r2Q1yToO96ysjBPPfvF3SLM8Zw5g+e7Taq5rzpVdx/7ZyU+mTbgmFO+H9PdR7H19578p2/rOOB\nHpvuTBpRxt+/8f7O4+rFS6koDbBuySX95h3PH1buZHGcoYpnzuThnZOpiv0+fnlNDecfH+0CO/P2\nZ9nbFH2P+mtJXfLTF9m0r5lBRX5ag2H8PsnoQ/L1+ccn3FDHGMPkm5b1e/+737+0c5OdZOxvbu9s\nZW3/wcKUhpRGIoYp3+7K/+mvzePtPU3dWjWZ8t8rNvPTFe8C8MOPvo9PnjGx2/lfPL+F/1y+iUe+\ncBazJyUekZQrbnp0Hb9fuZNhZUWsvsWef0REVhljahwSLadG4ixgiTHmEuv4JgBjzA+S3ZOpkYjV\n5B794tmcPrEyrXuNMRxtD7GuvpEnVr9HU1uQZev2Jrz2t/8yh3nWDz+eB17bwXf+0rt/8aefPIUL\njh/FsLKitMZkNzS38afa3WxtOMrSdXuSbpdYXhLgpW++n8oeozl2HDzG+f/5Qre4GaMr+NalM5g5\ndiijh5SkJY8xhvX1TaytP9JtpE9P+nNOX/GLl7utjxTwCf9z1WmcP2MkpQE/7aEIwUiEQUV+jIFg\nOEJZsb/bR+2Pnz+LOZP7/oGHwpHOZaVjzBw7hBsvnM6fanfx7DsNnDllOH9Y1P8M3Z60dIRYX9/E\n7EmV+H3Ctv1H+cCP/955fs7k4Qln/n7zz2v4Y220m+ruT88mGI4wvnIQOw+1MHF4GVNHlfPiu/v5\n0kNvdbtv5bcvZNSQUs6547lecxSSUVES4K1bLk7qYH+j7hAfTzBcdNP3F/Dc2w2cOnEYY4emtsZT\n7Ld3/Ohyvnf5LEYMLmbr/mMcbQ8xoXIQLcEwB5rbKQ74mFw1mH/+v9dpbou2fJwazBDDGMPCO//R\nuezGudOq+NIHpjFjdAXDyoqYe/uzNDS3p2zQsknPis/fv3FBxqPEvGwkPgYsMMZ8zjr+DDDXGPOl\nZPfYNRIAZcV+SuJqQBETbdKVBHz0fi+E5rZgn3vWfvbsar5xyQwOt3T06ZCKcaw9xEnfXd4trrTI\nR7Hfh4hQHPDhEwhHol0CEWMIRwyRiInKakyf3VW/+NTp+H1wyUljUnrRn9/UwLW/fqOXPAGfj5KA\nD79PKLI+JsYYDGAMdIQj+ATagxH8fuFISzBB6tGX+7hhgzrTSIW/rX2v18fQJ9Gy6klxwNf5PNL9\ncTe1BRN+eAHWLpnv2Iij5Rv28vnfrQKio2YSOUXDEcPUb/dfgwf4wgVTueuFrVx//tTOWdPGGBqa\n2/nNK3V87eLjCfiE/UfbKSsOcNcLWwhFDA+9tpOTxg3hoc+d2a/fp+7AMS74rxf4+vzj+fz5U9Mq\nv3iOtYeYe/uzaQ9K2HzbpRnn2R+rdx3hil+8nPBcz1ZlPqmtO9Q5t2PooCJeWfwBBmfQ+1DwRkJE\nFgGLACZOnDh7x44daef1p9pdjK8sY3NDM3UHWghFoh+VmKpFfl/nMtIx7YXoR2lIaYChZUU0tgQ5\nc8oIyksDVJYVM6VqcEYO1hg7D7awZX8zWxuO0dDcRjAcNQixD56I4PeBX8QKR/9EoKk1yPjKMsZX\nDuKU8cOoLCvunLmZCcYY1uxuZPO+ZlqDYXYcbLEMQZhQ2NARjiBE845pXGQZ2pKAj7ZgmKkjy6mp\nHs7kqsEMHeTMx3V/czsrtx9i6/6jdIQiDC4J4PdFDVNMhsPHOth24Bi3XTGLUUP6X0oiEcYY1u5u\nZOv+o7QGw1x04uhuyyrkksaWIK9tP0gkYghFDD4RQpEI+5raaGoNceWcCYyvLKM9FKYkkJ4zOJ/s\naWxl095mGprbGTG4uFO3itIAZcV+/D5hfX0juw+38sULpqXt6M6Ew8c6WPH2Phqa22luCxEKR/jG\nghmue67v7G3i0LEOzp5aldH9XjYSOetuUhRFGag4bSRyObrpDWC6iEwWkWLgSuCJHOavKIqipEnO\nWhIAIrIQ+BngB+4zxtzWz/X7gfT7m6JUAf2PMfQOhaYPqE5eodB0KjR9oLtOk4wxvUfUZEhOjUQu\nEZFaJ5tc+abQ9AHVySsUmk6Fpg9kVyddlkNRFEVJihoJRVEUJSmFbCTuzbcADlNo+oDq5BUKTadC\n0weyqFPB+iQURVEU+xRyS0JRFEWxScEZCa+tNCsidSKyTkRWi0itFTdcRJ4Rkc3W/8q462+ydNsk\nIpfExc+20tkiIndKjhajEZH7RKRBRNbHxTkmv4iUiMjDVvzrIlKdJ52WiEi9VU6rreHcntBJRCaI\nyPMislFENojIV6x4z5ZTHzp5uZxKRWSliKyxdLrVis9vORljCuaP6PyLrcAUoBhYA8zMt1z9yFwH\nVPWI+xGw2AovBn5ohWdaOpUAky1d/da5lcCZRFfSeBK4NEfyzwNOB9ZnQ37gi8DdVvhK4OE86bQE\n+HqCa12vEzAWON0KVxBdjXmml8upD528XE4ClFvhIuB1S668llPWPyK5/APOApbHHd8E3JRvufqR\nuY7eRmITMNYKjwU2JdIHWG7pPBZ4Jy7+KuCeHOpQTfcPqmPyx66xwgGiE4YkDzol+/h4Rqc4WR4n\numS/58spgU4FUU5AGfAmMDff5VRo3U3jgF1xx7utODdjgBUiskqiixsCjDbGxHYR2guMtsLJ9Btn\nhXvG5wsn5e+8xxgTAhqB/jctzg5fFpG1VndUrMnvKZ2s7oXTiNZSC6KceugEHi4nEfGLyGqgAXjG\nGJP3cio0I+FFzjXGnApcCtwgIvPiT5qoyffsEDSvyx/HXUS7MU8F9gA/zq846SMi5cAjwFeNMU3x\n57xaTgl08nQ5GWPC1vdgPDBHRGb1OJ/zcio0I1EPTIg7Hm/FuRZjTL31vwF4DJgD7BORsQDW/wbr\n8mT61VvhnvH5wkn5O+8RkQAwFEi8H2UWMcbss37AEeCXRMupm3wWrtRJRIqIfkwfNMY8akV7upwS\n6eT1cophjDkCPA8sIM/lVGhGwlMrzYrIYBGpiIWB+cB6ojJfY112DdH+Vqz4K60RCpOB6cBKqyna\nJCJnWqMYro67Jx84KX98Wh8DnrNqUzkl9iO1+DDRcgIP6GTl/yvgbWPMT+JOebackunk8XIaKSLD\nrPAgoj6Wd8h3OeXKsZSrP2Ah0ZEOW4Gb8y1PP7JOITo6YQ2wISYv0T7CZ4HNwApgeNw9N1u6bSJu\nBBNQQ/QHsRX4OblzsP2eaLM+SLTv8zon5QdKgT8BW4iO2JiSJ51+B6wD1lo/tLFe0Qk4l2gXxVpg\ntfW30Mvl1IdOXi6nk4G3LNnXA7dY8XktJ51xrSiKoiSl0LqbFEVRFAdRI6EoiqIkRY2EoiiKkhQ1\nEoqiKEpS1EgoiqIoSVEjoSiKoiRFjYSiKIqSFDUSiqIoSlL+P7rI499wB01DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8924711d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "streams = latent_to_stream(alpha, beta, env)\n",
    "for i in range(3):\n",
    "    plt.subplot(3, 1, i+1)\n",
    "    plt.plot(streams[:, i]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 11 into shape (2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-f2cc98bba0f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mon_segments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mon_segs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_on_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_segs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_segs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-16-f2cc98bba0f6>\u001b[0m in \u001b[0;36mget_on_clusters\u001b[0;34m(intensity)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mshort_offs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_segments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mmin_sil_duration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mremove_elem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mshort_offs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshort_offs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mon_segments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_segments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove_elem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mon_segments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 11 into shape (2)"
     ]
    }
   ],
   "source": [
    "from swissknife.streamtools import streams as st\n",
    "\n",
    "def get_on_clusters(intensity):\n",
    "    # get the below/above threshold\n",
    "    min_sil_duration = 600 #samples\n",
    "    silences = np.where(intensity==0)[0]\n",
    "    no_silences = np.where(intensity>0)[0]\n",
    "    onoff = np.ones_like(intensity, dtype=np.short)\n",
    "    onoff[silences]=0\n",
    "    \n",
    "    # get segments of song/silence\n",
    "    onsets = np.where(np.diff(onoff)==1)[0]\n",
    "    offsets = np.where(np.diff(onoff)==-1)[0] \n",
    "    if onsets[0] > offsets[0]:\n",
    "        onsets = np.concatenate([np.array([0]), onsets])\n",
    "    if onsets[-1] > offsets[-1]:\n",
    "        offsets = np.concatenate([offsets, np.array([intensity.size])])\n",
    "    on_segments = np.vstack([onsets, offsets]).T\n",
    "    \n",
    "    # filter 'silence' shorter that what a syllable should last\n",
    "    short_offs = (np.where(np.diff(on_segments.flatten()) < min_sil_duration)[0])\n",
    "    remove_elem = np.vstack([short_offs, short_offs + 1]).T.flatten()\n",
    "    on_segments = np.delete(on_segments, remove_elem).reshape([-1, 2])\n",
    "    \n",
    "    return on_segments\n",
    "\n",
    "on_segs = get_on_clusters(alpha)\n",
    "plt.plot(alpha)\n",
    "plt.plot(on_segs, np.ones_like(on_segs)*0.3, '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  729,  3888],\n",
       "       [ 5166, 10045],\n",
       "       [11147, 15701],\n",
       "       [18050, 19215],\n",
       "       [20173, 28002]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "on_segs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/scipy/signal/signaltools.py:3398: FutureWarning:  Note: Decimate's zero_phase keyword argument will default to True in a future release. Until then, decimate defaults to one-way filtering for backwards compatibility. Ideally, always set this argument explicitly.\n",
      "  \"explicitly.\", FutureWarning)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'on_segs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-515cae0d7122>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mend\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     19\u001b[0m \u001b[0mduration\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mon_segs\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     21\u001b[0m     \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mstep_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mstep_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'on_segs' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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N1bQd76ahOpLqcDBc0Yjl7IHk0ooMA3V3zdfGsOfIySFVDdbXJIMhu5t1oK0j2fg+ZVzl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\nhv8POsZ7zt0XQVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd89218f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_samples = 30\n",
    "\n",
    "msec_alpha = st.decimate(alpha, step_samples)\n",
    "msec_beta = - st.decimate(beta, step_samples)\n",
    "msec_env = st.decimate(env, step_samples)\n",
    "msec_beta[msec_beta>0] = 0\n",
    "msec_env[msec_env<0] = 0\n",
    "plt.plot(msec_beta)\n",
    "\n",
    "# make the ms-sampled templates per syllable\n",
    "# get the syllable starts\n",
    "#mot_templates = pd.DataFrame\n",
    "\n",
    "out_a = []\n",
    "out_b = []\n",
    "out_e = []\n",
    "start = []\n",
    "end = []\n",
    "duration = []\n",
    "for i, seg in enumerate(on_segs):\n",
    "    start.append(seg[0]//step_samples)\n",
    "    end.append(seg[1]//step_samples)\n",
    "    duration.append(end[i] - start[i])\n",
    "    out_a.append(msec_alpha[start[i]:end[i]])\n",
    "    out_b.append(msec_beta[start[i]:end[i]])\n",
    "    out_e.append(msec_env[start[i]:end[i]])\n",
    "    \n",
    "mot_templates = {\n",
    "    field: var for field, var in zip(['alpha', 'beta', 'env', 'start', 'duration', 'end', 'syl_type'],\n",
    "                                    [out_a, out_b, out_e, start, duration, end, range(i)])\n",
    "}\n",
    "\n",
    "import pickle\n",
    "out_fn = os.path.join(fn['folders']['stim'], 'syl_templates_ms.p')\n",
    "pickle.dump(mot_templates, open(out_fn, 'wb'), protocol=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': [array([  0.00000000e+00,   2.26702925e-07,   2.74469865e-04,\n",
       "           1.17631186e-02,   1.05490377e-01,   3.04279847e-01,\n",
       "           3.42689062e-01,   2.59230510e-01,   3.11072846e-01,\n",
       "           2.97160495e-01,   2.84333322e-01,   3.08161589e-01,\n",
       "           2.83514259e-01,   3.01525906e-01,   2.93152314e-01,\n",
       "           2.92772814e-01,   2.99121170e-01,   2.90031516e-01,\n",
       "           2.98972893e-01,   2.92310149e-01,   2.95581891e-01,\n",
       "           2.95746474e-01,   2.92969269e-01,   2.97048223e-01,\n",
       "           2.93025725e-01,   2.95953055e-01,   2.94635318e-01,\n",
       "           2.94371770e-01,   2.95777845e-01,   2.93866460e-01,\n",
       "           2.95662672e-01,   2.94425650e-01,   2.94911008e-01,\n",
       "           2.95127266e-01,   2.94431432e-01,   2.95311638e-01,\n",
       "           2.94520887e-01,   2.95036322e-01,   2.94866231e-01,\n",
       "           2.94727801e-01,   2.95065878e-01,   2.94663631e-01,\n",
       "           2.95009674e-01,   2.94797162e-01,   2.94851964e-01,\n",
       "           2.94932086e-01,   2.94769775e-01,   2.94952446e-01,\n",
       "           2.94802016e-01,   2.94888425e-01,   2.94873579e-01,\n",
       "           2.94829825e-01,   2.94906931e-01,   2.94824505e-01,\n",
       "           2.94889426e-01,   2.94854880e-01,   2.94857162e-01,\n",
       "           2.94880145e-01,   2.94843860e-01,   2.94880819e-01,\n",
       "           2.94853018e-01,   2.94866541e-01,   2.94867473e-01,\n",
       "           2.94855733e-01,   2.94872664e-01,   2.94856195e-01,\n",
       "           2.94868000e-01,   2.94862851e-01,   2.94861564e-01,\n",
       "           2.94867435e-01,   2.94859596e-01,   2.94866888e-01,\n",
       "           2.94861922e-01,   2.94863810e-01,   2.94864770e-01,\n",
       "           2.94861884e-01,   2.94865487e-01,   2.94862279e-01,\n",
       "           2.94864346e-01,   2.94863692e-01,   2.94863094e-01,\n",
       "           2.94864493e-01,   2.94862848e-01,   2.94864250e-01,\n",
       "           2.94863400e-01,   2.94863605e-01,   2.94863947e-01,\n",
       "           2.94863277e-01,   2.94864023e-01,   2.94863414e-01,\n",
       "           2.94863759e-01,   2.94863706e-01,   2.94863521e-01,\n",
       "           2.94863839e-01,   2.94863503e-01,   2.94863765e-01,\n",
       "           2.94863628e-01,   2.94863634e-01,   2.94863730e-01,\n",
       "           2.94863581e-01,   2.94863731e-01,   2.94863619e-01,\n",
       "           2.94863673e-01,   2.94863678e-01,   2.94863629e-01]),\n",
       "  array([ -5.19844887e-05,   1.07086450e-04,   3.19797461e-04,\n",
       "           1.56473808e-02,   1.22886318e-01,   3.20226063e-01,\n",
       "           3.33261797e-01,   2.59045618e-01,   3.15826033e-01,\n",
       "           2.91745253e-01,   2.88197403e-01,   3.06377084e-01,\n",
       "           2.83350091e-01,   3.02979405e-01,   2.91221306e-01,\n",
       "           2.94533899e-01,   2.97916122e-01,   2.90538567e-01,\n",
       "           2.99111449e-01,   2.91714749e-01,   2.96378667e-01,\n",
       "           2.94997009e-01,   2.93489594e-01,   2.96839516e-01,\n",
       "           2.92939961e-01,   2.96242837e-01,   2.94264901e-01,\n",
       "           2.94706765e-01,   2.95557928e-01,   2.93940603e-01,\n",
       "           2.95719455e-01,   2.94283664e-01,   2.95080914e-01,\n",
       "           2.94980521e-01,   2.94522055e-01,   2.95287632e-01,\n",
       "           2.94487689e-01,   2.95104560e-01,   2.94789062e-01,\n",
       "           2.94791603e-01,   2.95028991e-01,   2.94670287e-01,\n",
       "           2.95027874e-01,   2.94764722e-01,   2.94886803e-01,\n",
       "           2.94904531e-01,   2.94784549e-01,   2.94951310e-01,\n",
       "           2.94792427e-01,   2.94903704e-01,   2.94857948e-01,\n",
       "           2.94841634e-01,   2.94901135e-01,   2.94824187e-01,\n",
       "           2.94894335e-01,   2.94847745e-01,   2.94864131e-01,\n",
       "           2.94875127e-01,   2.94846074e-01,   2.94881329e-01,\n",
       "           2.94850559e-01,   2.94869847e-01,   2.94864385e-01,\n",
       "           2.94857846e-01,   2.94871849e-01,   2.94855804e-01,\n",
       "           2.94869211e-01,   2.94861330e-01,   2.94862924e-01,\n",
       "           2.94866554e-01,   2.94859880e-01,   2.94867135e-01,\n",
       "           2.94861335e-01,   2.94864504e-01,   2.94864175e-01,\n",
       "           2.94862246e-01,   2.94865397e-01,   2.94862137e-01,\n",
       "           2.94864627e-01,   2.94863377e-01,   2.94863352e-01,\n",
       "           2.94864346e-01,   2.94862871e-01,   2.94864327e-01,\n",
       "           2.94863267e-01,   2.94863747e-01,   2.94863836e-01,\n",
       "           2.94863335e-01,   2.94864020e-01,   2.94863374e-01,\n",
       "           2.94863821e-01,   2.94863643e-01,   2.94863569e-01,\n",
       "           2.94863816e-01,   2.94863501e-01,   2.94863786e-01,\n",
       "           2.94863599e-01,   2.94863662e-01,   2.94863710e-01,\n",
       "           2.94863590e-01,   2.94863734e-01,   2.94863609e-01,\n",
       "           2.94863686e-01,   2.94863666e-01,   2.94863638e-01,\n",
       "           2.94863696e-01,   2.94863630e-01,   2.94863684e-01,\n",
       "           2.94863653e-01,   2.94863659e-01,   2.94863674e-01,\n",
       "           2.94863646e-01,   2.94863676e-01,   2.94863653e-01,\n",
       "           2.94863665e-01,   2.94863664e-01,   2.94863656e-01,\n",
       "           2.94863669e-01,   2.94863656e-01,   2.94863666e-01,\n",
       "           2.94863661e-01,   2.94863661e-01,   2.94863665e-01,\n",
       "           2.94863659e-01,   2.94863665e-01,   2.94863660e-01,\n",
       "           2.94863662e-01,   2.94863663e-01,   2.94863661e-01,\n",
       "           2.94863663e-01,   2.94863661e-01,   2.94863663e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863663e-01,\n",
       "           2.94863661e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01]),\n",
       "  array([  2.79189889e-04,  -3.16997413e-04,   3.30464111e-04,\n",
       "           5.03248201e-03,   6.63875773e-02,   2.53411988e-01,\n",
       "           3.58786269e-01,   2.70052164e-01,   2.93374609e-01,\n",
       "           3.11345003e-01,   2.77418926e-01,   3.08613409e-01,\n",
       "           2.87618033e-01,   2.95486824e-01,   2.98824110e-01,\n",
       "           2.88836346e-01,   3.00854855e-01,   2.90318154e-01,\n",
       "           2.97249418e-01,   2.94700630e-01,   2.93277108e-01,\n",
       "           2.97401076e-01,   2.92240784e-01,   2.96873654e-01,\n",
       "           2.93850681e-01,   2.94843564e-01,   2.95672395e-01,\n",
       "           2.93662469e-01,   2.96049848e-01,   2.94000502e-01,\n",
       "           2.95253254e-01,   2.94938246e-01,   2.94453873e-01,\n",
       "           2.95422276e-01,   2.94337575e-01,   2.95227180e-01,\n",
       "           2.94719539e-01,   2.94802364e-01,   2.95065955e-01,\n",
       "           2.94606512e-01,   2.95095460e-01,   2.94711905e-01,\n",
       "           2.94914508e-01,   2.94903276e-01,   2.94765252e-01,\n",
       "           2.94981271e-01,   2.94762153e-01,   2.94926294e-01,\n",
       "           2.94847146e-01,   2.94840597e-01,   2.94910953e-01,\n",
       "           2.94810226e-01,   2.94907811e-01,   2.94838168e-01,\n",
       "           2.94868221e-01,   2.94876302e-01,   2.94841180e-01,\n",
       "           2.94887785e-01,   2.94844598e-01,   2.94873869e-01,\n",
       "           2.94862898e-01,   2.94857006e-01,   2.94874249e-01,\n",
       "           2.94852840e-01,   2.94871831e-01,   2.94859659e-01,\n",
       "           2.94863432e-01,   2.94867068e-01,   2.94858719e-01,\n",
       "           2.94868486e-01,   2.94860191e-01,   2.94865190e-01,\n",
       "           2.94864020e-01,   2.94861956e-01,   2.94865952e-01,\n",
       "           2.94861525e-01,   2.94865123e-01,   2.94863100e-01,\n",
       "           2.94863389e-01,   2.94864501e-01,   2.94862609e-01,\n",
       "           2.94864603e-01,   2.94863053e-01,   2.94863857e-01,\n",
       "           2.94863833e-01,   2.94863255e-01,   2.94864143e-01,\n",
       "           2.94863251e-01,   2.94863912e-01,   2.94863600e-01,\n",
       "           2.94863563e-01,   2.94863857e-01,   2.94863444e-01,\n",
       "           2.94863841e-01,   2.94863560e-01,   2.94863678e-01,\n",
       "           2.94863715e-01,   2.94863569e-01,   2.94863760e-01,\n",
       "           2.94863585e-01,   2.94863702e-01,   2.94863660e-01,\n",
       "           2.94863634e-01,   2.94863705e-01,   2.94863618e-01,\n",
       "           2.94863695e-01,   2.94863646e-01,   2.94863660e-01,\n",
       "           2.94863676e-01,   2.94863642e-01,   2.94863682e-01,\n",
       "           2.94863648e-01,   2.94863668e-01,   2.94863664e-01,\n",
       "           2.94863655e-01,   2.94863671e-01,   2.94863653e-01,\n",
       "           2.94863668e-01,   2.94863660e-01,   2.94863661e-01,\n",
       "           2.94863665e-01,   2.94863658e-01,   2.94863666e-01,\n",
       "           2.94863659e-01,   2.94863663e-01,   2.94863663e-01,\n",
       "           2.94863660e-01,   2.94863664e-01,   2.94863660e-01,\n",
       "           2.94863663e-01,   2.94863662e-01,   2.94863661e-01,\n",
       "           2.94863663e-01,   2.94863661e-01,   2.94863663e-01,\n",
       "           2.94863661e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01]),\n",
       "  array([ -4.67275499e-07,  -2.41779634e-07,   3.81941422e-05,\n",
       "           3.69932368e-03,   5.45881512e-02,   2.32284982e-01,\n",
       "           3.60527474e-01,   2.77487414e-01,   2.86005031e-01,\n",
       "           3.15529036e-01,   2.76664630e-01,   3.07107613e-01,\n",
       "           2.90222101e-01,   2.92891868e-01,   3.00679729e-01,\n",
       "           2.87976349e-01,   3.00803186e-01,   2.91014644e-01,\n",
       "           2.96242718e-01,   2.95696114e-01,   2.92537610e-01,\n",
       "           2.97756737e-01,   2.92272330e-01,   2.96551407e-01,\n",
       "           2.94313193e-01,   2.94394016e-01,   2.95994122e-01,\n",
       "           2.93523259e-01,   2.96012667e-01,   2.94162931e-01,\n",
       "           2.95037648e-01,   2.95137968e-01,   2.94318637e-01,\n",
       "           2.95472801e-01,   2.94365023e-01,   2.95147322e-01,\n",
       "           2.94818555e-01,   2.94714615e-01,   2.95122128e-01]),\n",
       "  array([  3.00512214e-04,  -5.01449532e-04,   6.63774922e-04,\n",
       "           7.51741268e-03,   8.47327869e-02,   2.80056400e-01,\n",
       "           3.52582818e-01,   2.62954836e-01,   3.02745529e-01,\n",
       "           3.04669784e-01,   2.80032787e-01,   3.09225946e-01,\n",
       "           2.84983265e-01,   2.98738596e-01,   2.96066468e-01,\n",
       "           2.90530486e-01,   3.00330130e-01,   2.89869382e-01,\n",
       "           2.98313625e-01,   2.93430773e-01,   2.94388150e-01,\n",
       "           2.96693408e-01,   2.92455904e-01,   2.97094244e-01,\n",
       "           2.93351690e-01,   2.95426288e-01,   2.95178138e-01,\n",
       "           2.93959122e-01,   2.95982467e-01,   2.93874727e-01,\n",
       "           2.95493762e-01,   2.94673089e-01,   2.94668383e-01,\n",
       "           2.95302665e-01,   2.94353338e-01,   2.95294916e-01,\n",
       "           2.94605707e-01,   2.94921642e-01,   2.94973727e-01,\n",
       "           2.94653926e-01,   2.95094715e-01,   2.94676774e-01,\n",
       "           2.94967875e-01,   2.94849933e-01,   2.94804616e-01,\n",
       "           2.94962896e-01,   2.94759671e-01,   2.94944073e-01,\n",
       "           2.94822324e-01,   2.94864307e-01,   2.94894298e-01,\n",
       "           2.94817127e-01,   2.94910173e-01,   2.94829342e-01,\n",
       "           2.94879679e-01,   2.94865830e-01,   2.94848157e-01,\n",
       "           2.94885305e-01,   2.94842989e-01,   2.94878182e-01,\n",
       "           2.94857646e-01,   2.94861601e-01,   2.94871358e-01,\n",
       "           2.94853671e-01,   2.94872792e-01,   2.94857579e-01,\n",
       "           2.94865812e-01,   2.94783595e-01,   2.89079294e-01,\n",
       "           2.24233387e-01,   3.46642369e-02,  -6.29187488e-02,\n",
       "           2.70807542e-02,  -1.08095159e-03,  -1.48182149e-02,\n",
       "           1.69343121e-02,  -1.40931725e-02,   8.07653729e-03,\n",
       "          -1.55035491e-03,  -3.22671856e-03,   5.62046560e-03,\n",
       "          -5.91741295e-03,   4.73169967e-03,  -2.72021255e-03,\n",
       "           5.27436845e-04,   1.29522918e-03,  -1.16613869e-03,\n",
       "           3.08050316e-02,   1.66373038e-01,   3.49050497e-01,\n",
       "           3.07728938e-01,   2.65622304e-01,   3.22110549e-01,\n",
       "           2.80378428e-01,   2.98680748e-01,   2.99427526e-01,\n",
       "           2.85821925e-01,   3.04317293e-01,   2.87623084e-01,\n",
       "           2.98785271e-01,   2.94251988e-01,   2.92874995e-01,\n",
       "           2.98345997e-01,   2.91093773e-01,   2.97906512e-01,\n",
       "           2.93161052e-01,   2.95089814e-01,   2.95836330e-01,\n",
       "           2.93221957e-01,   2.96588842e-01,   2.93527740e-01,\n",
       "           2.95550254e-01,   2.94854395e-01,   2.94352483e-01,\n",
       "           2.95638611e-01,   2.94090248e-01,   2.95433293e-01,\n",
       "           2.94598924e-01,   2.94826081e-01,   2.95122477e-01,\n",
       "           2.94503466e-01,   2.95207104e-01,   2.94622898e-01,\n",
       "           2.94962922e-01,   2.94898563e-01,   2.94735320e-01,\n",
       "           2.95029860e-01,   2.94712015e-01,   2.94964495e-01,\n",
       "           2.94828069e-01,   2.94840059e-01,   2.94926347e-01,\n",
       "           2.94787502e-01,   2.94930192e-01,   2.94821907e-01,\n",
       "           2.94875547e-01,   2.94877725e-01,   2.94833441e-01,\n",
       "           2.94898328e-01,   2.94834670e-01,   2.94880727e-01,\n",
       "           2.94860181e-01,   2.94855846e-01,   2.94878070e-01,\n",
       "           2.94847985e-01,   2.94876206e-01,   2.94856796e-01,\n",
       "           2.94864390e-01,   2.94867818e-01,   2.94856860e-01,\n",
       "           2.94870707e-01,   2.94858275e-01,   2.94866371e-01,\n",
       "           2.94863709e-01,   2.94861520e-01,   2.94866845e-01,\n",
       "           2.94860516e-01,   2.94865956e-01,   2.94862619e-01,\n",
       "           2.94863475e-01,   2.94864740e-01,   2.94862184e-01,\n",
       "           2.94865058e-01,   2.94862693e-01,   2.94864050e-01,\n",
       "           2.94863819e-01,   2.94863129e-01,   2.94864343e-01,\n",
       "           2.94863046e-01,   2.94864067e-01,   2.94863524e-01,\n",
       "           2.94863559e-01,   2.94863922e-01,   2.94863350e-01,\n",
       "           2.94863932e-01,   2.94863495e-01,   2.94863707e-01,\n",
       "           2.94863722e-01,   2.94863537e-01,   2.94863804e-01,\n",
       "           2.94863545e-01,   2.94863730e-01,   2.94863649e-01,\n",
       "           2.94863629e-01,   2.94863721e-01,   2.94863598e-01,\n",
       "           2.94863713e-01,   2.94863635e-01,   2.94863664e-01,\n",
       "           2.94863679e-01,   2.94863634e-01,   2.94863691e-01,\n",
       "           2.94863640e-01,   2.94863673e-01,   2.94863662e-01,\n",
       "           2.94863653e-01,   2.94863675e-01,   2.94863649e-01,\n",
       "           2.94863671e-01,   2.94863658e-01,   2.94863661e-01,\n",
       "           2.94863666e-01,   2.94863656e-01,   2.94863668e-01,\n",
       "           2.94863658e-01,   2.94863663e-01,   2.94863663e-01,\n",
       "           2.94863660e-01,   2.94863665e-01,   2.94863659e-01,\n",
       "           2.94863664e-01,   2.94863661e-01,   2.94863661e-01,\n",
       "           2.94863663e-01,   2.94863661e-01,   2.94863663e-01,\n",
       "           2.94863661e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863661e-01,   2.94863662e-01,   2.94863661e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01,\n",
       "           2.94863662e-01,   2.94863662e-01,   2.94863662e-01])],\n",
       " 'beta': [array([ -1.28328453e-17,  -6.44199668e-08,  -3.17147186e-05,\n",
       "          -1.16838391e-03,  -1.04343798e-02,  -3.30904854e-02,\n",
       "          -4.73411934e-02,  -4.84628524e-02,  -5.89100451e-02,\n",
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       "          -1.04591509e-02,  -9.79021340e-03,  -1.07250999e-02,\n",
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       "          -1.35085851e-02,  -1.41462613e-02,  -1.35637441e-02,\n",
       "          -1.30666769e-02,  -1.18756410e-02,  -1.13610047e-02,\n",
       "          -1.21759648e-02,  -1.23766749e-02,  -1.39315650e-02,\n",
       "          -1.52042252e-02,  -1.61579673e-02,  -1.87695140e-02,\n",
       "          -2.27135609e-02,  -2.83725581e-02,  -3.01065105e-02,\n",
       "          -2.70095708e-02,  -2.41133806e-02,  -2.10643039e-02,\n",
       "          -1.76675299e-02,  -1.48376628e-02,  -1.27075457e-02,\n",
       "          -1.14735114e-02,  -1.05512407e-02,  -9.46335025e-03,\n",
       "          -8.53240744e-03,  -7.70880258e-03,  -7.50907224e-03,\n",
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       "          -6.17166093e-03,  -5.65956809e-03,  -6.02554908e-03,\n",
       "          -5.86194241e-03,  -5.85926517e-03,  -5.97459811e-03,\n",
       "          -5.81472831e-03,  -5.96286356e-03,  -5.85855213e-03,\n",
       "          -5.85747196e-03,  -5.36497142e-03,  -4.02749076e-03,\n",
       "          -3.57632304e-03,  -4.10524747e-03,  -3.90655857e-03,\n",
       "          -3.85712588e-03,  -4.02278387e-03,  -3.85322996e-03]),\n",
       "  array([  0.00000000e+00,  -1.04934687e-06,  -3.14484846e-05,\n",
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       "          -2.11680683e-01,  -2.05709061e-01,  -1.87854611e-01,\n",
       "          -1.77878066e-01,  -1.67026415e-01,  -1.56728478e-01,\n",
       "          -1.52510288e-01,  -1.50527198e-01,  -1.50072614e-01,\n",
       "          -1.36002859e-01,  -8.50947373e-02,  -2.12555649e-02,\n",
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       "          -8.52244665e-03,  -7.49771176e-03,  -7.18434490e-03,\n",
       "          -7.93794022e-03,  -4.86357449e-03,  -6.22387427e-03,\n",
       "          -5.88077721e-03,  -5.71717291e-03,  -6.17418571e-03,\n",
       "          -5.58413147e-03,  -6.16545002e-03,  -5.72705668e-03,\n",
       "          -5.95120132e-03,  -5.94848046e-03,  -5.77567910e-03,\n",
       "          -6.04280659e-03,  -5.77140349e-03,  -5.97449380e-03,\n",
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       "          -4.20815066e-03,  -3.55781433e-03,  -4.03807181e-03,\n",
       "          -3.97065649e-03,  -3.82679090e-03,  -4.01966051e-03,\n",
       "          -3.94550893e-03,  -4.60087797e-03,  -5.92039287e-03,\n",
       "          -6.27600067e-03,  -6.36657569e-03,  -7.94248195e-03,\n",
       "          -8.40621498e-03,  -8.58347800e-03,  -1.03420208e-02,\n",
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       "          -1.91294499e-02,  -2.64854618e-02,  -3.09007593e-02,\n",
       "          -3.16515992e-02,  -3.17092662e-02,  -3.13101907e-02,\n",
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       "          -1.11557113e+00,  -1.97755126e+00,  -2.00685906e+00,\n",
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       "          -2.11368577e-02,  -3.12198844e-02,  -2.69014860e-02,\n",
       "          -2.55398421e-02,  -3.10219107e-02,  -2.37625298e-02,\n",
       "          -3.04784296e-02,  -2.59650125e-02,  -2.75881892e-02,\n",
       "          -2.86092465e-02,  -2.58358146e-02,  -2.92467949e-02,\n",
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       "          -1.09712715e-01,  -1.50527320e-01,  -1.50702358e-01,\n",
       "          -1.42419200e-01,  -1.41812435e-01,  -1.37468725e-01,\n",
       "          -1.32079164e-01,  -1.26236567e-01,  -1.20137157e-01,\n",
       "          -1.14984226e-01,  -1.09584009e-01,  -1.04555659e-01,\n",
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       "          -8.57930082e-02,  -8.15976724e-02,  -7.74716052e-02,\n",
       "          -7.35325910e-02,  -6.97279702e-02,  -6.60176109e-02,\n",
       "          -6.16677952e-02,  -5.37866853e-02,  -4.04967288e-02,\n",
       "          -2.67534048e-02,  -1.91945855e-02,  -1.71803781e-02,\n",
       "          -1.69016039e-02,  -1.60076713e-02,  -1.51345758e-02,\n",
       "          -1.52173744e-02,  -1.37848610e-02,  -1.33537627e-02,\n",
       "          -1.48044802e-02,  -1.55650087e-02,  -1.64776656e-02,\n",
       "          -1.81469324e-02,  -2.34585557e-02,  -3.01600679e-02,\n",
       "          -3.62345606e-02,  -4.19273786e-02,  -4.37471968e-02,\n",
       "          -4.33744777e-02,  -4.31777581e-02,  -4.26361226e-02,\n",
       "          -4.08993226e-02,  -3.87791627e-02,  -3.74669545e-02,\n",
       "          -3.57365815e-02,  -3.41831564e-02,  -3.27723020e-02,\n",
       "          -3.11383649e-02,  -2.98248690e-02,  -2.83357182e-02]),\n",
       "  array([ -1.29018045e-05,   0.00000000e+00,  -2.90066587e-05,\n",
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       "          -1.47553536e-02,  -1.20355808e-02,  -1.12839562e-02,\n",
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       "          -1.01029268e-02,  -9.66265720e-03,  -9.86514416e-03,\n",
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       "          -1.38153680e-02,  -1.38600712e-02,  -1.36208259e-02,\n",
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       "          -5.82015053e-03,  -5.90583864e-03,  -5.93771416e-03,\n",
       "          -5.83402690e-03,  -5.96092396e-03,  -5.84847838e-03,\n",
       "          -5.92351934e-03,  -5.89445547e-03,  -5.88136159e-03,\n",
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       "          -5.88598406e-03,  -5.89760634e-03,  -5.90545123e-03,\n",
       "          -5.88471449e-03,  -5.90988656e-03,  -5.88793228e-03,\n",
       "          -5.90165946e-03,  -5.89782488e-03,  -5.89309261e-03,\n",
       "          -5.90313184e-03,  -5.89166637e-03,  -5.90122006e-03,\n",
       "          -5.89562813e-03,  -5.89672845e-03,  -5.89934954e-03,\n",
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       "          -5.89786585e-03,  -5.89764721e-03,  -5.89625811e-03,\n",
       "          -5.89851291e-03,  -5.89618632e-03,  -5.89795889e-03,\n",
       "          -5.89707382e-03,  -5.89704870e-03,  -5.89776315e-03,\n",
       "          -5.89670865e-03,  -5.89774716e-03,  -5.89699267e-03,\n",
       "          -5.89733254e-03,  -5.89739862e-03,  -5.89703947e-03,\n",
       "          -5.89752870e-03,  -5.89706805e-03,  -5.89738628e-03,\n",
       "          -5.89726022e-03,  -5.89720637e-03,  -5.89738369e-03,\n",
       "          -5.89715838e-03,  -5.89736141e-03,  -5.89722855e-03,\n",
       "          -5.89727299e-03,  -5.89730778e-03,  -5.89722161e-03,\n",
       "          -5.89735309e-03,  -5.89052630e-03]),\n",
       "  array([  0.00000000e+00,  -3.97400067e-09,  -1.75258746e-06,\n",
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       "          -3.90370758e-03,  -3.92669826e-03,  -3.96011665e-03,\n",
       "          -3.89216926e-03,  -3.96877801e-03,  -3.90561927e-03,\n",
       "          -3.94192590e-03,  -3.93567673e-03,  -3.91730372e-03,\n",
       "          -3.94968780e-03,  -3.91507433e-03,  -3.94232910e-03,\n",
       "          -3.92783415e-03,  -3.92878115e-03,  -3.93843884e-03]),\n",
       "  array([ -3.83598311e-06,   0.00000000e+00,  -8.73211305e-06,\n",
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       "          -3.93716683e-03,  -3.92517824e-03,  -3.93671817e-03,\n",
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       "          -3.92883391e-03,  -3.93437267e-03,  -3.92927175e-03,\n",
       "          -3.93270305e-03,  -3.93144625e-03,  -3.93071157e-03,\n",
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       "          -3.93105131e-03,  -3.93147881e-03,  -3.93192551e-03,\n",
       "          -3.93092734e-03,  -3.93208591e-03,  -3.93110792e-03,\n",
       "          -3.93169189e-03,  -3.93156201e-03,  -3.93131072e-03,\n",
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       "          -3.93145103e-03,  -3.93148137e-03,  -3.93161606e-03,\n",
       "          -3.93139044e-03,  -3.93162654e-03,  -3.93146290e-03,\n",
       "          -3.92906821e-03,  -3.82698808e-03,  -3.06785653e-03,\n",
       "          -1.32974438e-03,  -5.26305039e-07,   0.00000000e+00,\n",
       "          -2.68233837e-05,  -1.70504622e-05,   0.00000000e+00,\n",
       "          -2.96108477e-05,   0.00000000e+00,   0.00000000e+00,\n",
       "          -1.47646728e-05,   0.00000000e+00,  -1.48494720e-05,\n",
       "           0.00000000e+00,  -1.35470752e-06,  -3.89134093e-06,\n",
       "           0.00000000e+00,  -8.13557288e-06,  -1.39993889e-04,\n",
       "          -2.96914348e-03,  -1.68317987e-02,  -3.44145162e-02,\n",
       "          -3.09586078e-02,  -2.67093578e-02,  -3.19127171e-02,\n",
       "          -2.83295137e-02,  -2.96716657e-02,  -3.00172743e-02,\n",
       "          -2.86152574e-02,  -3.03303322e-02,  -2.88863698e-02,\n",
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       " 'duration': [105, 162, 152, 39, 261],\n",
       " 'end': [129, 334, 523, 640, 933],\n",
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       "          0.61636683,  0.61463824,  0.60598236,  0.60259958,  0.59681195,\n",
       "          0.59168025,  0.60101829,  0.60434878,  0.6092987 ,  0.6102372 ,\n",
       "          0.61456594,  0.63070343,  0.64054241,  0.65577165,  0.67027395,\n",
       "          0.6723226 ,  0.67894541,  0.68894538,  0.70187875,  0.70917224,\n",
       "          0.70092459,  0.69661171,  0.69585522,  0.69342333,  0.69009668,\n",
       "          0.68222386,  0.68213402,  0.68568368,  0.68047102,  0.68237636,\n",
       "          0.68819783,  0.6960082 ,  0.71286015,  0.72787449,  0.7478038 ,\n",
       "          0.77788957,  0.81028885,  0.83185574,  0.845684  ,  0.86055776,\n",
       "          0.86329921,  0.84800452,  0.8206018 ,  0.78696006,  0.75914866,\n",
       "          0.74775075])],\n",
       " 'start': [24, 172, 371, 601, 672],\n",
       " 'syl_type': [0, 1, 2, 3]}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mot_templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd891eca6d0>,\n",
       " <matplotlib.lines.Line2D at 0x7fd891eca8d0>,\n",
       " <matplotlib.lines.Line2D at 0x7fd891ecaa10>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7V3DnnDt5cMGDNJpGNu7bGPugZuYxHuZumcspU05h0AuD/L0bW1qtu5aBkwYy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\n3TG9dUWfN+cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8928727d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(latent_to_stream(alpha, beta, env*10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd891d8d4d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z80pEwp1REYURarav/kdFVhGc2TQD2/ceCLndzmamddV0uG7RqAGKjaeLaPpo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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd891f170d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reload(zf)\n",
    "song_synth = zf.finch(vocal_pars, latent_to_stream(alpha, beta*2, env*10))\n",
    "plt.plot(song_synth[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E44WUa7OsXHWScj6l4mITZMSJjWXHlMotUqMxRvVdyukOD3pegCqFxRB3udzC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\nC3T35jj5xcdJB5XZe8/mkuleC1l320BOBogsQeoaeR4jw0VoVnnnn/4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oxSF530cV1WtQ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\nsbjMzQnGQ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nvXD//Zz9loX0+tPqTz7GHqW9vo+siJ4FLPHXMr0K0hsjWYTOEoRC2eCsYo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\n6A5ao9FoNJoBRHfQGo1Go9EMIP8PwpW0S2o+4UcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd891fa8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectr_bos, ax = plot_spectrogram(song_synth[:, 0]*1000, \n",
    "                                   0, 0, 44100,\n",
    "                                   n_window = 512, n_overlap=512-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig_path = es.file_names(bird, '{:03d}'.format(syn_sess))\n",
    "fig_fname = os.path.join(fig_path['folders']['stim'], 'synthesis_smooth_a.eps')\n",
    "fig_fname\n",
    "ax.figure.savefig(fig_fname, format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'folders': {'kai': '/mnt/cube/kai/results/z040/001',\n",
       "  'prb': '/mnt/cube/earneodo/bci_zf/probes',\n",
       "  'proc': '/mnt/cube/earneodo/bci_zf/proc_data/z040/001',\n",
       "  'raw': '/usr/local/experiment/raw_data/z040/001',\n",
       "  'rw': '/mnt/cube/earneodo/bci_zf/raw_data/z040/001',\n",
       "  'ss': '/mnt/cube/earneodo/bci_zf/ss_data/z040/001',\n",
       "  'stim': '/mnt/cube/earneodo/bci_zf/stim_data/z040/001',\n",
       "  'templ': '/mnt/cube/earneodo/bci_zf/templates',\n",
       "  'tmp': '/usr/local/experiment/tmp_data/z040/001'},\n",
       " 'locations': {'archive': '/mnt/cube/earneodo/archive/bci_zf',\n",
       "  'experiment': '/mnt/cube/earneodo/bci_zf',\n",
       "  'experiment_local': '/usr/local/experiment',\n",
       "  'repos': '/mnt/cube/earneodo/repos',\n",
       "  'scratch': '/usr/local/experiment/scratchpad',\n",
       "  'store': '/Data/bci_zf'},\n",
       " 'structure': {'cand': 'experiment.mot.h5',\n",
       "  'evt': 'experiment.kwe',\n",
       "  'kk_par': 'params.prm',\n",
       "  'kk_prb': '*.prb',\n",
       "  'ks_cfg': 'config.m',\n",
       "  'ks_map': 'chanMap.mat',\n",
       "  'ks_mas': 'master.m',\n",
       "  'ks_par': 'params.py',\n",
       "  'mic': 'experiment-rec_000.mic.wav',\n",
       "  'par': 'experiment.par.yml',\n",
       "  'sng': 'experiment.sng.kwe',\n",
       "  'ss_bin': 'experiment.dat',\n",
       "  'ss_lfp': 'experiment.lfp.kwd',\n",
       "  'ss_par': 'experiment.par.yml',\n",
       "  'ss_raw': 'experiment.raw.kwd',\n",
       "  'stm': 'experiment.stm.kwe',\n",
       "  'structure': 'experiment',\n",
       "  'sts': 'experiment-rec_000.sts.wav'}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.file_names(bird, '{:03d}'.format(syn_sess))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'folders': {'kai': '/mnt/cube/kai/results/z040/',\n",
       "  'prb': '/mnt/cube/earneodo/bci_zf/probes',\n",
       "  'proc': '/mnt/cube/earneodo/bci_zf/proc_data/z040/',\n",
       "  'raw': '/usr/local/experiment/raw_data/z040/',\n",
       "  'rw': '/mnt/cube/earneodo/bci_zf/raw_data/z040/',\n",
       "  'ss': '/mnt/cube/earneodo/bci_zf/ss_data/z040/',\n",
       "  'stim': '/mnt/cube/earneodo/bci_zf/stim_data/z040/',\n",
       "  'templ': '/mnt/cube/earneodo/bci_zf/templates',\n",
       "  'tmp': '/usr/local/experiment/tmp_data/z040/'},\n",
       " 'locations': {'archive': '/mnt/cube/earneodo/archive/bci_zf',\n",
       "  'experiment': '/mnt/cube/earneodo/bci_zf',\n",
       "  'experiment_local': '/usr/local/experiment',\n",
       "  'repos': '/mnt/cube/earneodo/repos',\n",
       "  'scratch': '/usr/local/experiment/scratchpad',\n",
       "  'store': '/Data/bci_zf'},\n",
       " 'structure': {'cand': 'experiment.mot.h5',\n",
       "  'evt': 'experiment.kwe',\n",
       "  'kk_par': 'params.prm',\n",
       "  'kk_prb': '*.prb',\n",
       "  'ks_cfg': 'config.m',\n",
       "  'ks_map': 'chanMap.mat',\n",
       "  'ks_mas': 'master.m',\n",
       "  'ks_par': 'params.py',\n",
       "  'mic': 'experiment-rec_000.mic.wav',\n",
       "  'par': 'experiment.par.yml',\n",
       "  'sng': 'experiment.sng.kwe',\n",
       "  'ss_bin': 'experiment.dat',\n",
       "  'ss_lfp': 'experiment.lfp.kwd',\n",
       "  'ss_par': 'experiment.par.yml',\n",
       "  'ss_raw': 'experiment.raw.kwd',\n",
       "  'stm': 'experiment.stm.kwe',\n",
       "  'structure': 'experiment',\n",
       "  'sts': 'experiment-rec_000.sts.wav'}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.file_names(bird)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '/mnt/cube/earneodo/bci_zf/stim_data/z040/001/synth_bos_export.dat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-7f3af392abad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m                                              \u001b[0ms_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                              \u001b[0mnew_s_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m44100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                                              file_base='synth_bos_export')\n\u001b[0m",
      "\u001b[0;32m/mnt/cube/earneodo/repos/swissknife/swissknife/bci/synthetic.pyc\u001b[0m in \u001b[0;36mload_syn_stream\u001b[0;34m(bird, syn_sess, s_f, new_s_f, file_base)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0mstim_folder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0met\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbird\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'folders'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stim'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0msyn_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstim_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msyn_sess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_base\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.dat'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mstream_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msyn_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_s_f\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m    856\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 858\u001b[0;31m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'U'\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    859\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/mnt/cube/earneodo/bci_zf/stim_data/z040/001/synth_bos_export.dat'"
     ]
    }
   ],
   "source": [
    "# convert parameters from kai format to mine (2 to 2+1/2)\n",
    "latent_par = syn.load_syn_stream(bird, syn_sess=syn_sess, \n",
    "                                             s_f=30000, \n",
    "                                             new_s_f=44100, \n",
    "                                             file_base='synth_bos_export')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.where(latent_par[:,3]<0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(latent_par[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(latent_par[:,0]/400000)\n",
    "plt.plot(0.15 - latent_par[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(latent_par[:,3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.vstack([latent_par[:, 0], latent_par[:, 1]]).T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_par = syn.load_syn_stream(bird, syn_sess=syn_sess, \n",
    "                                             s_f=30000, \n",
    "                                             new_s_f=44100, \n",
    "                                             file_base='synth_bos_export')\n",
    "def latent_to_stream(onof, beta, alpha):\n",
    "    onof[onof>.3] = .3\n",
    "    onof[onof<0] = 0\n",
    "    alpha[alpha<0]=0\n",
    "    return np.vstack([0.15 - onof, -beta, alpha]).T\n",
    "\n",
    "par_stream = latent_to_stream(latent_par[:, 1], latent_par[:, 2], latent_par[:, 3])\n",
    "plt.plot(par_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(par_stream[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "par_stream[par_stream[:,0]>0, 1] = .15\n",
    "plt.plot(par_stream[:,1])\n",
    "plt.plot(par_stream[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "par_stream.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "song_synth_after = zf.finch(vocal_pars, par_stream)\n",
    "plt.plot(song_synth_after[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(song_synth_after[:,0])"
   ]
  }
 ],
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