eval_synthesized-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-05-06 22:01:05,984 root         INFO     Computer: passaro\n"
     ]
    }
   ],
   "source": [
    "# read h5file with simmulated zf song\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.DEBUG)\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.threadtools import threadedfunction as td\n",
    "from swissknife.streamtools import spectral as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sweep_id = '002'\n",
    "sweep_name = 'sweep_'+sweep_id\n",
    "file_name_base = 'finch_'+sweep_id \n",
    "sweep_folder = os.path.join(es.file_names('')['folders']['tmp'], 'syn_sweeps')\n",
    "sweep_file_name = '{}.h5'.format(file_name_base)\n",
    "sweep_file_path = os.path.join(sweep_folder, sweep_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-05-06 22:01:12,605 h5tools      DEBUG    Translating group sweep_pars into its own dictionary\n",
      "2017-05-06 22:01:12,610 h5tools      DEBUG    Subgroup sys\n",
      "2017-05-06 22:01:12,612 h5tools      DEBUG    Translating group sys into its own dictionary\n",
      "2017-05-06 22:01:12,616 root         INFO     Tables are in /sweep_002/synth/23500\n",
      "2017-05-06 22:01:12,619 root         INFO     Groups: [(u'alpha_beta', <HDF5 dataset \"alpha_beta\": shape (160000, 2), type \"<f8\">), (u'pre_in', <HDF5 dataset \"pre_in\": shape (160000, 4410), type \"<f8\">), (u'pre_out', <HDF5 dataset \"pre_out\": shape (160000, 4410), type \"<f8\">)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'sys', <HDF5 group \"/sweep_002/vocal_pars/sys\" (0 members)>)]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from swissknife.streamtools import streams as st\n",
    "\n",
    "def load_sweep_pars(sweep_file, sweep_name):\n",
    "    sweep_group = sweep_file['/{}'.format(sweep_name)]\n",
    "    print sweep_group['vocal_pars'].items()\n",
    "    sweep_pars = h5tools.group_2_dict({}, sweep_group['vocal_pars'], 'sweep_pars')\n",
    "    return sweep_pars['sweep_pars']\n",
    "\n",
    "\n",
    "class ParSweep(object):\n",
    "    def __init__(self, sweep_file, sweep_name, streams=['pre_in', 'pre_out'], transient_ms=50):\n",
    "        self.sweep_file = sweep_file\n",
    "        self.pars = load_sweep_pars(sweep_file, sweep_name)\n",
    "        tables_path = '/{0}/synth/{1:d}'.format(sweep_name,\n",
    "                                                 int(self.pars['sys']['gamma']))\n",
    "        logger.info('Tables are in {}'.format(tables_path))\n",
    "        self.sweep_tables_group = sweep_file[tables_path]\n",
    "        logger.info('Groups: {}'.format(self.sweep_tables_group.items()))\n",
    "\n",
    "        self.ab = pd.DataFrame(self.sweep_tables_group['alpha_beta'][()])\n",
    "        self.ab.columns = ['a', 'b']\n",
    "        self.transient_samples = int(transient_ms*0.001*self.pars['sys']['s_f'])\n",
    "        self.syn_streams = {stream: st.H5Data(self.sweep_tables_group[stream], \n",
    "                                            self.pars['sys']['s_f']) for stream in streams}\n",
    "        \n",
    "    def synthetic(self, row, rows=0, stream='pre_out'):\n",
    "        #get index of alpha, beta\n",
    "        return self.syn_streams[stream].get_chunk(row, row + rows, \n",
    "                                                  np.arange(self.transient_samples, \n",
    "                                                            self.syn_streams[stream].n_chans)\n",
    "                                                           )      \n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def regularize(u, cut_off=75, logarithmic=True):\n",
    "    cut_db = 1./np.exp(cut_off/10)\n",
    "    \n",
    "    y_thresh = np.repeat(np.amax(u, axis = 1, keepdims=True), u.shape[-1], axis=1)*cut_db\n",
    "    \n",
    "    if logarithmic:\n",
    "        u[u < y_thresh] = np.amin(y_thresh)\n",
    "        z = np.log(u)\n",
    "    else:\n",
    "        u[u < y_thresh] = np.amin(y_thresh)\n",
    "        z = u\n",
    "    \n",
    "    return z\n",
    "\n",
    "def normalize(u):\n",
    "    u_max = np.repeat(np.amax(u, axis = 1, keepdims=True), u.shape[-1], axis=1)\n",
    "    u_min = np.repeat(np.amin(u, axis = 1, keepdims=True), u.shape[-1], axis=1)\n",
    "    u_range = u_max - u_min\n",
    "    return (u - u_min)/u_range\n",
    "\n",
    "# with h5py.File(sweep_file_path, 'r') as sweep_file:\n",
    "#     #s_p = load_sweep_pars(sweep_file, sweep_name)\n",
    "#     p_s = ParSweep(sweep_file, sweep_name)\n",
    "#     one_stream = p_s.synthetic(153915)\n",
    "    \n",
    "sweep_file = h5py.File(sweep_file_path, 'r')\n",
    "p_s = ParSweep(sweep_file, sweep_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f06002634d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nw4SETWkyj82yUH7zcAoD2odv0231LZZR87gROmaIw4tnrE59bYeOFDKcLC9h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\nEP5s7PBfzeztwN+b2fIQwnzOqY+GEL7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06260b3110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_s.ab.plot.scatter('a', 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22000</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.033812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22001</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.034264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22002</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.034723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22003</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.035188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22004</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.035659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22005</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.036137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22006</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.036622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22007</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.037113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22008</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.037612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22009</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.038117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22010</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.038629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22011</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.039149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22012</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.039675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22013</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.040209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22014</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.040751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22015</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.041300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22016</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.041856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22017</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.042420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22018</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.042993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22019</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.043573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22020</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.044161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22021</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.044757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22022</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.045362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22023</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.045975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22024</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.046597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22025</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.047227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22026</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.047866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22027</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.048514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22028</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.049171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22029</th>\n",
       "      <td>-0.146835</td>\n",
       "      <td>-0.049837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156637</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.163982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156638</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.194139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156639</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.224715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156640</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.255717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156641</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.287151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156642</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.319022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156643</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.351337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156644</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.384101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156645</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.417321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156646</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.451004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156647</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.485156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156648</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.519783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156649</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.554892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156650</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.590489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156651</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.626582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156652</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.663178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156653</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.700283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156654</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.737904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156655</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.776049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156656</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.814724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156657</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.853938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156658</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.893698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156659</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.934011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156660</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-2.974886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156661</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.016329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156662</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.058349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156663</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.100954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156664</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.144152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156665</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.187951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156666</th>\n",
       "      <td>-0.149873</td>\n",
       "      <td>-3.232360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b\n",
       "22000  -0.146835 -0.033812\n",
       "22001  -0.146835 -0.034264\n",
       "22002  -0.146835 -0.034723\n",
       "22003  -0.146835 -0.035188\n",
       "22004  -0.146835 -0.035659\n",
       "22005  -0.146835 -0.036137\n",
       "22006  -0.146835 -0.036622\n",
       "22007  -0.146835 -0.037113\n",
       "22008  -0.146835 -0.037612\n",
       "22009  -0.146835 -0.038117\n",
       "22010  -0.146835 -0.038629\n",
       "22011  -0.146835 -0.039149\n",
       "22012  -0.146835 -0.039675\n",
       "22013  -0.146835 -0.040209\n",
       "22014  -0.146835 -0.040751\n",
       "22015  -0.146835 -0.041300\n",
       "22016  -0.146835 -0.041856\n",
       "22017  -0.146835 -0.042420\n",
       "22018  -0.146835 -0.042993\n",
       "22019  -0.146835 -0.043573\n",
       "22020  -0.146835 -0.044161\n",
       "22021  -0.146835 -0.044757\n",
       "22022  -0.146835 -0.045362\n",
       "22023  -0.146835 -0.045975\n",
       "22024  -0.146835 -0.046597\n",
       "22025  -0.146835 -0.047227\n",
       "22026  -0.146835 -0.047866\n",
       "22027  -0.146835 -0.048514\n",
       "22028  -0.146835 -0.049171\n",
       "22029  -0.146835 -0.049837\n",
       "...          ...       ...\n",
       "156637 -0.149873 -2.163982\n",
       "156638 -0.149873 -2.194139\n",
       "156639 -0.149873 -2.224715\n",
       "156640 -0.149873 -2.255717\n",
       "156641 -0.149873 -2.287151\n",
       "156642 -0.149873 -2.319022\n",
       "156643 -0.149873 -2.351337\n",
       "156644 -0.149873 -2.384101\n",
       "156645 -0.149873 -2.417321\n",
       "156646 -0.149873 -2.451004\n",
       "156647 -0.149873 -2.485156\n",
       "156648 -0.149873 -2.519783\n",
       "156649 -0.149873 -2.554892\n",
       "156650 -0.149873 -2.590489\n",
       "156651 -0.149873 -2.626582\n",
       "156652 -0.149873 -2.663178\n",
       "156653 -0.149873 -2.700283\n",
       "156654 -0.149873 -2.737904\n",
       "156655 -0.149873 -2.776049\n",
       "156656 -0.149873 -2.814724\n",
       "156657 -0.149873 -2.853938\n",
       "156658 -0.149873 -2.893698\n",
       "156659 -0.149873 -2.934011\n",
       "156660 -0.149873 -2.974886\n",
       "156661 -0.149873 -3.016329\n",
       "156662 -0.149873 -3.058349\n",
       "156663 -0.149873 -3.100954\n",
       "156664 -0.149873 -3.144152\n",
       "156665 -0.149873 -3.187951\n",
       "156666 -0.149873 -3.232360\n",
       "\n",
       "[8000 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_s.ab.loc[(p_s.ab['a'] > -0.15) & (p_s.ab['a'] < -0.146)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same spectrogram as used for actual data\n",
    "def spectrogram(x, s_f, n_perseg = None, n_overlap=None, cut_off=90, f_min=0., f_max=10000.):\n",
    "    f, t, s = scipy.signal.spectrogram(x, fs=s_f, window=('tukey', 0.25), \n",
    "                         nperseg=n_perseg, \n",
    "                         noverlap=n_overlap, \n",
    "                         nfft=None, \n",
    "                         detrend='constant', return_onesided=True, scaling='density', axis=-1, mode='psd')\n",
    "\n",
    "    s[s<np.max(s)/np.exp(cut_off/10)]=np.max(s)/np.exp(cut_off/10)\n",
    "    f_filter = np.where((f>f_min) & (f<f_max))\n",
    "    return f[f_filter], t, s[f_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 160/160 [00:12<00:00, 12.92it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "n_streams = p_s.ab.shape[0]\n",
    "f_bins = 512\n",
    "n_samples = f_bins * 2 # resolution of fft is given by n of samples\n",
    "s_f = p_s.pars['sys']['s_f']\n",
    "f_min = 150\n",
    "f_max = 7500\n",
    "\n",
    "aux_spectra = np.zeros([n_streams, f_bins], dtype=np.float32)\n",
    "rows_chunk = 1000\n",
    "for i in tqdm(np.arange(int(n_streams/rows_chunk))):\n",
    "#for i in tqdm(np.arange(1)):\n",
    "    x_i = p_s.synthetic(i*rows_chunk, rows=rows_chunk)[:, :n_samples]\n",
    "    f, t, s = spectrogram(x_i.flatten(), s_f, n_perseg=n_samples, n_overlap=0, f_min=f_min, f_max=f_max)\n",
    "    aux_spectra[i*rows_chunk:(i+1)*rows_chunk, :f.size] = s.T\n",
    "\n",
    "all_spectra = aux_spectra[:, :f.size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_spectra = normalize(all_spectra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f05f64a4750>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q5v2kqnK+k6rysifwEeAf6ve5pH6+BPhD4FzgY8ArgM9ExFMppS9RVl9b3gQs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\nBglJktSYQUKSJDVmkJAkSY39f5E3A4LL6LUEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05f72ccb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(f, all_spectra[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/local/experiment/tmp_data/syn_sweeps/syn_spectral_sweep_002_0512.pickle\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "syll_data = pd.DataFrame({\n",
    "        'bird': ['synth' for i in range(all_spectra.shape[0])],\n",
    "        'sess': [sweep_name for i in range(all_spectra.shape[0])],\n",
    "        'a': np.array(p_s.ab['a']).tolist(),\n",
    "        'b': np.array(p_s.ab['b']).tolist(),\n",
    "        'spectral': normalize(all_spectra).tolist()\n",
    "    })\n",
    "\n",
    "spectral_synth = {\n",
    "    'f': f,\n",
    "    'spectral_pd': syll_data,\n",
    "    's_f': s_f,\n",
    "    'syn_pars': p_s.pars\n",
    "}\n",
    "\n",
    "#syll_data.to_pickle(os.path.join(sweep_folder, 'spectral_pandas_{}.pickle'.format(sweep_name)))\n",
    "store_path = os.path.join(sweep_folder, 'syn_spectral_{0}_{1:04d}.pickle'.format(sweep_name, f_bins))\n",
    "\n",
    "with open(store_path, 'wb') as output: \n",
    "    pickle.dump(spectral_synth, output)\n",
    "print(store_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0cbc8bf8d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cLNau9XARt2Bhln6/kJdf9sc77oD581v+rPHjvVph1vh4164KFiIi5ULBIqaS\ng8WqVf4Yt2ABHixSDa585RXYfnsPCjfe2PxnfPqp7wuS3A0CGrwpIlJOFCxiKnnlzTgHi8GD4bXX\nmh5/5RUfiHnKKXDDDT7mIp0JE/xxv/2avqauEBGR8qFgEVPlVLHYZx944QXYsKHh2MKF8P77vh7F\n2LGwYIGPt0hn/HjYaSfo2bPpawoWIiLlQ8EipsotWCxaBK+/3nBs6lR/3HlnGDgQjj4arrmmcfiI\nhOAVi+RpphEFCxGR8qFgEVN1dbB+vX9BQ7Coqytdm9LZdVdv17PPNhx7+WUfdLnVVv78vPPgvffg\n0Uebvv+tt3yMRarxFeCfozEWIiLlQcEipqIAEa2+GeeKRfv2MHKkr0MReeUVr1bU1P8LGznSv26+\nuen7J0zw+91zz9Sf36VL/rdmFxGRwlCwiKkoWETdIXEOFuDdIc8919DVEQWLRKNGwVNPNZ2aOn48\n7LEHdOiQ+rO1w6mISPlQsIipcgwWixbBG2/AvHnw0UdNg8Xhh/v9jB/fcGzNGu9CSdcNAgoWIiLl\nRMEipsotWIwc6W1+7jmvVkDDDqWRgQNh223hoYcajr34Iixfnn7gJihYiIiUEy3pHVPlFizat/dB\nnM8+65WL7t1hs82annfEEXD77T4otbbWqxebbAJDh6b/7K5d/VEDOEVE4k8Vi5hq184fyyVYgHeH\nPP+87xEyYkTTpbnBg8WCBTBlij+fMAH2399DRjqqWIiIlA8Fi5hKVbEwg7ZtS9emluyzD3z+OTz5\nZNPxFZFddvFFsB56CJYs8RDSXDcIKFiIiJQTdYXEVKpg0b596ipAXIwc6ZWWNWvSB4vaWjjsMHj4\nYdhtN+8SaW7gJihYiIiUE1UsYipdsIizDh18nAU0HbiZ6Igj4O23fSv1LbeELbZo/nPbtPHP1hgL\nEZH4U7CIqVQLZMU9WAAceqivttm3b/pzDjjA7+XJJ1uuVkS0rLeISHlQsIipcqxYgC/d/eqrzZ/T\nqVPDuIqWxldEFCxERMqDgkVMJQeL1avLI1i0aQOdO7d83rHH+v2k2iY9FQULEZHyoGARU+VascjU\nmDG+rfomm2R2voKFiEh5ULCIqVTrWFRSsDBrfhxGMu1wKiJSHhQsYqqmxtesqNRgkS1VLEREyoOC\nRYy1a6dgEVGwEBEpDzkFCzM7y8w+MLOVZjbFzJpZtaDR+/Yws7VmNi2X61abujoFi4iChYhIecg6\nWJjZKODo0o4AAAAgAElEQVQa4FJgKDAdeMLMerTwvm7AncCEHNpZlZKDRTSgsxp16aIxFiIi5SCX\nisVY4JYQwl0hhHeAM4AVwCktvO9m4B5gSg7XrEqqWDTo2lUVCxGRcpBVsDCztsBw4KnoWAgh4FWI\n3Zp537eBLYCf59bM6lRXV34rbxZKly6wbBls2FDqloiISHOyrVj0AGqBeUnH5wG9U73BzAYBVwAn\nhBD0YyELqlg0iDYiW768tO0QEZHmFXR3UzOrwbs/Lg0hvB8dzvT9Y8eOpVu3bo2OjR49mtGjR+ev\nkTGmYNEgcYfT6M8iItVu3LhxjBs3rtGxJUuWlKg1LttgsQBYD/RKOt4LmJvi/C7AzsBOZnZD/bEa\nwMxsDXBQCOHZdBe77rrrGDZsWJZNrBwKFg2iMPHFF9ktrCUiUslS/bI9bdo0hg8fXqIWZdkVEkJY\nC0wF9o+OmZnVP5+U4i1fANsDOwE71n/dDLxT/+cXc2p1lVCwaNC1qz9qAKeISLzl0hVyLXCHmU0F\nXsJniXQE7gAwsyuBviGEk+sHdr6V+GYz+wxYFUJ4uzUNrwYKFg0Su0JERCS+sp5uGkK4HzgPuAx4\nFdgBODiEML/+lN7AgLy1sIpFK2+GoGBRjGAxfz7svz/MSx6aLCIiGctp5c0Qwo0hhM1DCB1CCLuF\nEF5JeO3bIYS0m2GHEH4eQqjegRNZiCoW69b5NEsFi8IukjV+PDz9NDzzTOGuISJS6bRXSIxFwWLV\nKn9ezcGifXuorS1sxWJS/SihV18t3DVERCqdgkWMRQtkKVj4NuuFXn1z8mR/VLAQEcmdgkWMRRWL\naABnNQcLKOxGZMuXw/TpMGAATJvm41pERCR7ChYxpq6QxgoZLF5+GdavhzPOgM8/hzlzCnMdEZFK\np2ARYwoWjRVyh9NJk7yr5YQT/Lm6Q0REcqNgEWMKFo0VcozF5Mmw667wpS9B9+4KFiIiuVKwiDEF\ni8YK1RUSggeL3Xf3QaLDhvk4CxERyZ6CRYxFC2QpWLjmgsWaNXDiiTBrVvafO2OGj6vYbTd/PnSo\nKhYiIrlSsIix5IpFXV1p21NqzY2xeO01uOceePzx7D83mma6667+OHQofPSRhw0REcmOgkWM1dX5\nqpsrVvhzVSzSVyyiCsPbOexAM2kSbLcdbLSRPx86tPFniohI5hQsYiyqUES/pVd7sGhu8GYUAt55\nJ/vPnTy5oRsEYNAg6NxZ4yxERHKhYBFjUbBYsqTx82oVVSxSLV6Va8Xiiy/gzTcbB4uaGthxR1Us\nRERyoWARY4nBoq7OZyxUsy5dvGsoWok0sm4dvP467LSTj41Ytizzz3zxRQ8qu+/e+LgGcIqI5EbB\nIsYSg0W1d4NA+h1O33vPB7iOHt3wPFEIsHhx6s+cNAk23hi23rrx8aFD/XOyCSkiIqJgEWsKFo1F\nwSJ5nEVUWRg1yh+Tu0P+/W/o0wc+/bTpZz72GOy7r3d/JBo61APJ66+3vt0iItVEwSLGFCwa69rV\nH1MFi803h802g759mw7gHD/eKxoPPND4+Ny53hXy9a83vdZ220HbthrAKSKSLQWLGFOwaKy5ikU0\nRXSbbZoGi4kT/fEf/2h8/JFHvFJx6KFNr9WuHey8M9x5J6xd2/q2i4hUCwWLGGvXzh8VLFyqMRYh\nNA0WiV0hy5c3vP7ss40XvXroIdhjD+jRI/X1fvc7X3jrpz/N622IiFQ0BYsYU8WisVQViw8/hEWL\nGoLFkCG+RPe6df78lVd8O/SrroING+DBB/34ihXeRXLEEemvN2IE/PKX/t6nn87//YiIVCIFixhL\nXCBLwcIXrYLGwSIauJlYsVizBj74wJ9PnOhjM/bbD77yFfjnP/34hAk+7qK5YAFw/vk+uPOkk2DB\ngvzdi4hIpVKwiDEFi8ZqajxcJAeLTTf1QZvgFQtoGGcxaRKMHAm1tXDMMV6l+OIL7wYZPLjpNNNU\n17z7bl8747TTUi/OJSIiDRQsYiwKFqtXK1hEkvcLicZPRIuH9e3r57zzjnd9TJrUsPjV0Ud7NeOh\nh+Dhh1uuVkT69oXbb/dulPvuy+/9iIhUGgWLGEtcwlvBwvXq5cEgGoSZOHATPGBEAzjffdfHX+yx\nh782YADssgv87Gfw2WeZBwuAI4+EY4+Fc86BhQvzdjsiIhVHwSLGFCyauvVWH7C5xx4wdSrMmdM4\nWEDDlNOJE70rI9oOHbxq8f770L174/1BMnH99V49Ov/81t+HiEilUrCIsZoaaNPG/6xg4UaM8O6N\nNWt8MCY0Hyx22KFhNgn4OAuAww7zcRfZ6NMHrr4a/vQneOaZ3O9BRKSSKVjEXFS1ULBoMGiQh4sh\nQ7zysNVWjV8fMsS7QB59tOnmYltt5V0h556b27VPPRX23BO+8x1YuTK3zxARqWQKFjEXLZJV7Vum\nJ+vd2ysSr77adJ+Pbbbxx/nzG8ZXJLr0Ut8JNRc1NQ3dMb/4RW6fISJSyRQsYk4Vi/Q6dPABmcm2\n2qqhCym5YpEP22wDP/6xd4tokzIRkcYULGJOwSJ7bdvCwIE+TXSzzQpzjQsv9DUwTj/dV/YUERGn\nYBFzCha52X9/37U0Wt8i39q1g9tug5dfhhtuaPzahg0tv3/tWvj5z30siIhIJWlT6gZI8xQscpP8\nw74Qdt8dzjwTLrkEvvxlmDLFd1B991146aWGVUBTef11H0Q6fz784Q+Fb6uISLGoYhFzChbxduWV\nsNFGvhfJ5ZfDlltCz57w3e82X7mYM8cfb74Z3nqrOG0VESkGBYuYU7CIt65d4fHHfTXQ+fPh/vu9\ni+SFF+COO9K/b84cHwuy2WZw3nlFa66ISMEpWMScgkX8bb89HH64z1IBr16MGeOB4bPPUr9nzhzo\n189nljz2mIcTEZFKoGARcwoW5ek3v/GBo+mqEXPmQP/+cNRRsPfe8H//B+vWFbeNIiKFoGARcwoW\n5WnTTT1c3H03PPts09ejYGEG117rm6Zdf33RmykikncKFjEXrbypYFF+vvUtH0PxyCNNX4uCBcCw\nYb5r6nnnwT33FLWJIiJ5p+mmMaeKRfkyg8GDfTfVRCE0DhYA11wDixf72Ix27eC444rbVhGRfFGw\niDkFi/I2cCD85z+Njy1cCKtWNQ4WNTVw++0+zmL0aF+S/KijittWEZF8UFdIzClYlLctt4SZM71K\nEfn4Y39MDBbg27j/+c9w7LFw/PHw4ovFa6eISL4oWMScgkV5GzgQli9vPO00WhwrOViAVyruuguG\nD4djjoF584rTThGRfFGwiDkFi/K25Zb+OHNmw7E5c7w60bt36ve0awd//7t3i4wa5fuKiIiUCwWL\nmFOwKG9RsEgcwDlnDvTp4+Einb594W9/g4kTfSdV8HEZc+fC6tWFa6+ISGspWMRcFCyiRykvXbr4\n3iHJwSJVN0iyPff0NS6uu86DZYcOHkgGD4Z33ilcm0VEWkOzQmKurs73lKhRBCxb0QDOSLScdybO\nPht69IDPP/fNzjp1gp/8BL7yFV8fY+TIwrRZRCRXChYx97WvwZo1pW6FtMbAgU0rFgcdlNl7zXz6\naaJ99oEjjvA9Se67z/cpERGJCwWLmNt664Y+dilPW24JTz/d8DzTrpB0Nt4YnnwSvvlNDxiDB8O+\n+3rQGDDAx27U1vo4jXQDREVECkXBQqTABg6ETz+FFSt8psfSpa0LFuDjLf7+d/jXv2DCBA8uN9/c\n+Jx27eCCC+CSSxp2XhURKTQFC5ECGzjQHz/4wLs2oPXBArwqceyx/gXwySc+FmP9etiwAR54AK66\nyvcfuf5671aLri8iUigKFiIFljjlNJo2nI9gkaxvX/+KDBsGJ53kA0APP9y7TE480btQojaJiOSb\n5hqIFFifPh4o3n+/YdXNxABQSIMGweOPw/jxsOuu8KtfeQXlzDMbLzMuIpIvOQULMzvLzD4ws5Vm\nNsXMRjRz7lFm9qSZfWZmS8xskpllOCZepPyZNUw5nTMHevXy8Q/FvP4BB8Cdd/oS4dde6+Mxrruu\neG0QkeqRdbAws1HANcClwFBgOvCEmfVI85a9gCeBQ4BhwDPAw2a2Y04tFilD0ZTTjz8uTDdIpjp1\ngrFjfabR+efDE0+Uri0iUplyqViMBW4JIdwVQngHOANYAZyS6uQQwtgQwm9CCFNDCO+HEH4EzAA0\n+16qRmLFopTBInL55XDIIb4XyXvvlbo1IlJJsgoWZtYWGA48FR0LIQRgArBbhp9hQBdgYTbXFiln\nAwf6rJDZs+MRLGprfbZInz5w2GEwfXqpWyQilSLbikUPoBZI3sx5HpDpUjznA52A+7O8tkjZGjjQ\nV1B9++14BAuAbt3g4Yd92fidd4Yf/9g3OhMRaY2iTjc1s28CPwGOCCEsaOn8sWPH0q1bt0bHRo8e\nzejkNY5FYi6a3rlhQ3yCBcBWW8HUqXDlld498o9/+PTU/faDbbbRuhcicTdu3DjGjRvX6NiSJUtK\n1BpnIYs5Z/VdISuAY0IIDyUcvwPoFkI4qpn3Hg/cDhwbQni8hesMA6ZOnTqVYcOGZdw+kbhatQo6\ndvQpns884/t9xM1//+sDO595xlcI7d3blwy/8EKteyFSTqZNm8bw4cMBhocQphX7+ll1hYQQ1gJT\ngf2jY/VjJvYHJqV7n5mNBv4IHN9SqBCpRO3bN+xoGqeKRaLttvM9SBYv9tkiY8bAgw/6fjWnnNJ4\nIzURkXRymRVyLXC6mY0xs22Am4GOwB0AZnalmd0ZnVzf/XEn8H/Ay2bWq/6ra6tbL1JGoqW9M90y\nvVQ6dfLdV6+6ymeyXH01/Pvf3m3Srx/svz+cdRa89lqpWyoicZR1sAgh3A+cB1wGvArsABwcQphf\nf0pvYEDCW07HB3zeAHyS8PXb3JstUn4GDoTu3ctrQ7COHb17ZOZMGDfOKxebbAKPPOLjMN58s9Qt\nFJG4yWnwZgjhRuDGNK99O+n5vrlcQ6TSlPMeHR07wvHHNzxfvNjHiRx0EPznP+V7XyKSf9orRKRI\n9t8ffvSjUrciPzbayMdhRN0mc+eWukUiEhfa3VREctKrl29utscesP32MGAAdO7s62MceKBXOHr1\nKnUrRaTYVLEQkZxtvjk8+yycdhrsvjtssYUvBHb++T7Q89BD4emnS91KESkmVSxEpFUGDfLt2BN9\n/jn87W/wxz969eK3v4Xvf7807ROR4lLFQkTyrnt3OOMMmDIFzj0XfvADDxbr1pW6ZSJSaKpYiEjB\n1NbCNdf4IltnneWLbN17L3TVKjYiFUsVCxEpuO9+Fx57DCZOhK98BT78sNQtEpFCUbAQkaI48ECY\nPBmWLoVddoGXXip1i0SkENQVIiJFs+228OKLcOSRsPfeXr2orfWvbbeFn/4UunQpdStFpDVUsRCR\nourZ06egnnuuLw/euTO0bQs33eTrYUyYUOoWikhrqGIhIkXXvj1ceWXjY7NmwamnepfJ6afDOed4\nFcPMX1+7Fp57Dt56y4/vuCNsumnRmy4iLVCwEJFY2HxzX8nz1lvhoovgttt8jYwjj/R1MR54ABYu\nhDZtGqatDhgAd9zhG6KJSDyoK0REYqOmxte/mDcPHn3Ux2HcdRe88ILPLJk6FVatgnffhfvu8zBy\n/PHwySelbrmIRFSxEJHYqavz5cAPPTT161tv7V/77AM77QQnnOBjM2prIQRfO+MPf4Abb0z/GSJS\nGKpYiEjZ6tkTxo2D55+Hyy7zqazHHed7lXTtCocdBldf7WFDRIpDwUJEytree8PPfw6/+IUP6Hzy\nSfjnP+G11+Dii+GCC+Dkk2HlylK3VKQ6KFiISNm7+GLv8ujcGV55BY46ysdrXH45/PWvviHakCFw\n992wYUOpWytS2RQsRKTs1dbCww/D9Ok+9iLR6NF+fPhwGDMGhg3TVu4ihaRgISIVwaxhzYtkW28N\n//gHTJrkVY0DD4Tbby9u+0SqhYKFiFSN3XbzRbbOOMMX4frVrzSwUyTfNN1URKpKba1PRd10Ux+b\nMX++zxyp0a9ZInmh/5VEpOqYwc9+Br//PVx3Hey+u4/DEJHWU7AQkap19tm+BsayZT6487zz/M+p\nqMtEJDMKFiJS1b7yFZg2zdfBuOEGX1Qr2oskMnOm70ty7bWlaaNIOVGwEJGq166dj7d44gnfl+QX\nv2h4bdUqOPZY3wDtvPN8MzQRSU/BQkSk3l57NaziGa11ce65vlX7f/4Dxxzj+5JMm9bwnvnz4d//\n1sJbIhEFCxGRBBdf7Nuwn3CCD+y85RafRTJsGNx5J2y3HRx+uK+LMWoU9OsHX/saXHppqVsuEg8K\nFiIiCWpr4S9/8QrED3/oq3Weeqq/1rEjPPign3PssfD6674Wxk9/Cr/8pQcPkWqndSxERJL07u37\ni9x2m2+9nriiZ58+Pg7j009h1139tRDg44990a3NNvPt3AHWrvWBoB06pL9WCN79cvfd3t3Sp09B\nb02k4FSxEBFJYa+9/Id9p05NX9tsMxg5siFwmMFNN/l7jj4avvc9f71LF+jVy9fLWL++6eesXg0n\nnujBYt48r5CIlDsFCxGRPGjbFv7+d9h+e3jmGRg0CK66ysdqnHOOB42pU3379sWL4YMP4IADfKzG\nffd5MLn3Xhg/vtR3ItI66goREcmTjTbyBbeSnXwyfPe7sPPOjY/36OEhZLfdvEvkT3/yascbb0D7\n9r5Y18UXexfJgAHwpS/54NHTT4c2+u4tMaV/miIiBTZyJLzyCjz0kFcs2reHujoYMcLHc4B3p9x4\nI+y4ow8IPfhgOOkkH8vxjW/4tNbnn/dzVq5Ut4nEl4KFiEgRtG3r62A0Z8gQuOACuOIKX0tjl13g\nsce8WyVy9tk+tfX446Fv39za8tln0LNnbu8VaYnGWIiIxMiPfuTraFx2mc8+SQwV4IGjQwc4//zG\nx999F26/HRYsaP7zb7zRA0niIl8i+aRgISISIx06wOOPe8BINY5i4419UOhf/wrPPuvH/v5330Tt\n9NOhf3/vQpk8uel7V6zwwLJ+vS9Pro3VpBAULEREyszJJ/uAz7PPhosuguOO883TZs3yisbEib4V\n/B/+0Ph9f/gDfP45XH+9Dxp99NGSNF8qnIKFiEiZqanxnVjffhuuvtq/xo3z9TXOPx/+9z/4wQ98\ngOeUKf6eJUu80nHaaR5I9tvPz03eyVWktRQsRETK0NChcM89Xnk477zGq4PW1MBvfuOzTr7xDR93\n8dvfwvLl8OMf+7nXXOPjMm67rXT3IJVJwUJEpEwdf7yv9plK27a+8NbKlR4urrnG18jo189f32kn\n3wfl0kvhiy+K12apfAoWIiIVqn//hkGeGzb4eIxEv/ylL8K1++6+9bsGc0o+KFiIiFSwAw+EO+7w\nqajJa1f07++revbo4Vu/H3CAP9+woSRNlQqhYCEiUuHGjPFuk1SGDfNxGg8/7Kt87rmnLx1+1lnw\n9NPZVzEWLvRVQqV6KViIiFQ5M5+u+sYb3m1y7LHwyCOw//4+APTJJzMPGEcd5WFl0aKCNlliTMFC\nREQAqK2Fvff2GSSzZnnFol0737dk333h1Vebf/8bb/h+Jp984hWPOFi9Gv75T5+OK8WhYCEiIk2Y\neZiYONGrF59/7pup3Xpr+urFLbf4pmp//KP/IE/8YR4CvPiibxffknwMIn3zTZ8F06eP79Fywgkt\nByPJDwULERFJy8wHdr7yCpx6qm///q1v+fLgiZYtg7vu8nNOPhlGjfIf7B99BNOnw0EHeTDZcksf\nUHrvvbBqVdPrheCDSEeM8PflYu5cn+ny0EPe3tdfh8GDfUEwzXwpPAULERFpUV2db2D2l7/43iR7\n7NF4HMW4cb4A13e+42HkppugUyc/b+hQ+PBD75K4807vnhg92ndvXbOm8XWeeca7YObNg513hp/9\nrOk5LbnoIu/CeeMNuPJK+PKXfdXRp57yfViksBQsREQkYyec4BucffihD9RcvdqrADfdBIce6jNK\nwDdLu/tuDxfXX+9dE0cd5TNUnn/eu1j++1+4+ebGn3/55R5EZszwjdguv9wrHYsXZ9a+yZM9vFxx\nhbchcvjhvpjYBRf4JmxSOAoWIiKSlR128G6GKVO8W+Sll3z8wplnNj5v3319P5Ozz/aVQBPtvrt3\nm/z85w2VjylTvFpxySVeIfnZz/yzP/jAA01LgWDDBvj+931WyqmnNn7NzJc5f/NNX9dDCkfBosTG\nVclQZd1nZdF9VpZc7nOPPbxb5L774Otf9w3QDj44u8+47DLv5vjlL/35FVfANtvA0Uc3nDN0qI/H\nePxxX368OX/+M0yd6hWS2tqmr48YAbvtNo6f/MS7WnL1xRde+TjmGP972Gor+Pa3fWyH5BgszOws\nM/vAzFaa2RQzG9HC+fuY2VQzW2Vm75nZybk1t/LoG1dl0X1WFt1n84491vcgmTfPB0mm+mHenN69\n4cIL4fe/9/EXDz/s4yNqkn4yHXywj5W4/HIf37F4sYeH7baDzp1hxx39h/yFF8KJJ/oP+3Q6dhzH\nypWw+eZeYfnf/3xBr1tv9UGj/frB2LG+QVs6F13kW9AvXw6DBvng1ocfhq23huuug7Vrs/t7qDRZ\nBwszGwVcA1wKDAWmA0+YWY80528OPAI8BewI/A643cwOzK3JIiISF2PH+piJ//u/3N7/wx9Cr16+\nUdpmm8E3v5n6vPPP95kmY8ZA375+ve228+6SPfaApUu9cnDVVc1fr2NHDxM/+pGHma239impZ57p\n3SVHH+2VmG228QXC3nij8fsnTvTxJL/+tVdR7rgDfvc7eO89OOkk32l26FAPHdWqTQ7vGQvcEkK4\nC8DMzgC+BpwC/DrF+WcCM0MIF9Q/f9fMvlL/OeNzuL6IiMTInnvm/t6OHb0LZMwY715IHosRMfP1\nMc44A4YMgVNO8YpHLrp39+3jzzvPZ7OsWwdHHgmbbuqvX321V0auuAL22cdnk+y0kw9UPf102HXX\npuNJNtkEbrgBTjsNnnjCB61Wq6yChZm1BYYDV0THQgjBzCYAu6V520hgQtKxJ4Drsrm2iIhUphNP\n9K6J3Xdv/rxOnXymSb60b+9jI1IdP/FE7+I48EDYbz+YMMEXCpsxw8dxpOv2GTrUv6pZthWLHkAt\nkDzsZR4wOM17eqc5v6uZ1YUQVqd4T3uAt99+O8vmlZ8lS5Ywbdq0Ujej4HSflUX3WVnicJ+dOuW+\nIFY2sr3Xa67x5cn33tsX9DrpJK9wxPmfRcLPzvaluL6FLJYhM7M+wMfAbiGEFxOOXwXsFUJoUrUw\ns3eBP4UQrko4dgg+7qJjqmBhZt8E7snmRkRERKSRE0IIfy32RbOtWCwA1gO9ko73AtJNtJmb5vwv\n0lQrwLtKTgBmASkWfRUREZE02gOb4z9Liy6rYBFCWGtmU4H9gYcAzMzqn1+f5m2TgUOSjh1Ufzzd\ndT4Hip6yREREKsSkUl04l3UsrgVON7MxZrYNcDPQEbgDwMyuNLM7E86/GdjSzK4ys8Fm9j3g2PrP\nERERkQqS9XTTEML99WtWXIZ3abwGHBxCmF9/Sm9gQML5s8zsa/gskB8Ac4BTQwjJM0VERESkzGU1\neFNERESkOdorRERERPJGwUJERETyJnbBItsNzkrJzC42s5fM7Aszm2dm/zKzrVOcd5mZfWJmK8xs\nvJltlfR6nZndYGYLzGypmf3dzHomnbOxmd1jZkvMbJGZ3W5mJVk01swuMrMNZnZt0vGyv08z62tm\nd9e3cYWZTTezYUnnlPV9mlmNmf3CzGbW38P/zOzHKc4ru/s0sz3N7CEz+7j+3+gRpbovMxtgZo+a\n2XIzm2tmvzazvHzPbe4+zayN+WD5181sWf05d5qvQ1Qx95ni3Jvrz/lBJd6nmQ0xswfNbHH9f9cX\nzax/LO8zhBCbL2AUvm7FGGAb4BZgIdCj1G1L095/AycBQ4Av44t+zQI6JJxzYf09HAZsDzwAvA+0\nSzjnpvr37Y1v7DYJeCHpWo8B04Cdgd2B94C/lOCeRwAzgVeBayvpPoGNgA+A2/Gl6zcDDgC2qLD7\nvAT4DPgq8CXgaOAL4Oxyv8/6e7oM+Dq+5s4RSa8X5b7wX9rewNcR+DJwcP3f+S8LfZ9A1/rrHgMM\nAnYBpgAvJX1GWd9n0nlH4d+TPgJ+UGn3CQzE15G6EtgB2KL+33CPON5nQf7nbsVf7hTgdwnPDZ9F\nckGp25Zh+3sAG4CvJBz7BBib8LwrsBL4RsLz1cBRCecMrv+cXeqfD6l/PjThnIOBdUDvIt5fZ+Bd\nYD/gGRoHi7K/T+BXwHMtnFMJ9/kwcFvSsb8Dd1XYfW6g6TfootwXvnbPWhp/4/8usAhoU+j7THHO\nzvgPrP6Vdp9AP+DD+vZ+QEKwqJT7BMYBdzbznljdZ2y6Qqxhg7OnomPB76q5Dc7iZiMg4L8RYWZb\n4NNvE+/pC+BFGu5pZ3zab+I57+L/o0TnjAQWhRBeTbjWhPpr7VqIG0njBuDhEMLTiQcr6D4PB14x\ns/vNu7ammdlp0YsVdJ+TgP3NbBCAme0I7IFX4CrpPhsp8n2NBN4IISxIOOcJoBuwXZ5uKRvR96bF\n9c+HUwH3aWYG3AX8OoSQanOpsr/P+nv8GjDDzB6v/940xcy+nnBarO4zNsGC5jc4y3Fz3OKp/4//\nW+A/IYS36g/3xv+jNXdPvYA19d/g0p3TGy9H/X8hhPV4gCnK342ZHQ/sBFyc4uVKuc8tgTPxqsxB\neGnxejM7KaF9lXCfvwLuA94xszXAVOC3IYR7E9pXCfeZrJj3lW7zRSjyvZtZHf7f/K8hhGUJbaiE\n+7wIv48/pHm9Eu6zJ14tvhAP/wcC/wL+aWbRhvWxus+sF8iStG4EtsV/86so9QOEfgscEEJYW+r2\nFFAN3g/9k/rn081se+AMII+bNZfcKOCbwPHAW3hg/J2ZfRJCqKT7rHpm1gb4Gx6ovlfi5uSVmQ3H\nF/pGrwMAAAMxSURBVF2s9E3KowLAAyGEaOuM181sd/x70wulaVZ6capY5LLBWSyY2R+AQ4F9Qgif\nJrw0Fx8n0tw9zQXamVnXFs5JHt1bC2xCcf5uhgObAtPMbK2ZrcUHCJ1T/xvvPCrjPj8Fksupb+MD\nHKFy/nv+GvhVCOFvIYT/hhDuwVfGjapRlXKfyYp5X+k2X4Qi3XtCqBgAHJRQrYjaUO73+RX8+9JH\nCd+XNgOuNbOZCW0o9/tcgI+DaOl7U2zuMzbBov434WiDM6DRBmcl20ylJfWh4uvAviGEDxNfCyF8\ngP/HSLynrnh/VnRPU/F/NInnDMb/wUQbtU0GNjKzxGS+P/5N8kUKbwI+QngnYMf6r1eAvwA7hhBm\nUhn3OREf8JRoMDAbKuq/Z0c8xCfaQP33gwq6z0aKfF+TgS+bb38QOQhYgleJCiohVGwJ7B9CWJR0\nSiXc5134DIkdE74+wYPzwfXnlP191v9sfJmm35u2pv57E3G7z3yOZm3tF/ANYAWNp5t+Dmxa6ral\nae+N+GjZPfFUF321Tzjngvp7OBz/4fwAMIPG09tuxEcz74NXBybSdJrQv/Ef5iPw7pZ3gbtLeO/J\ns0LK/j7xgXur8d/cB+LdBUuB4yvsPv+MD+o6FP8N7yi87/WKcr9PoBP+A2YnPCydW/98QDHvCw9p\n0/HpfTvgP+jmAb8o9H3iXdwP4j90vkzj701tK+U+05zfaFZIpdwncCS+FMNp+Pems4E1wG5xvM+C\nfxPL4S/4e/hc3JV4etq51G1qpq0b8N/8kr/GJJ33MzxJr8BH2G6V9Hod8Hu85LUU/02jZ9I5G+EV\ngiV4mLkN6FjCe3+ahGBRKfeJ/7B9vf4e/guckuKcsr7P+m9i19Z/E1qO/2D9OUnTycrxPvEuulT/\nX/6p2PeF/5B/BFiGf3O+Cqgp9H3iYTH5tej5XpVyn2nOn0nTYFER9wl8C193Yjm+FsVhcb1PbUIm\nIiIieRObMRYiIiJS/hQsREREJG8ULERERCRvFCxEREQkbxQsREREJG8ULERERCRvFCxEREQkbxQs\nREREJG8ULERERCRvFCxEREQkbxQsREREJG/+H9scYDKQ0Y5aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d1c177e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = np.arange(f_bins)*p_s.pars['sys']['s_f']/f_bins/2\n",
    "plt.plot(f, all_spectra[i_stream, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f47a522e610>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pYBdw2SDvuxb4GfBwDd+zJl1dxY8rVSzGjOkfOvJSsZgxw3+lGSxiNG+qYiEi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\nWF2IlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47a53260d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normalize(u):\n",
    "    u_max = np.repeat(np.amax(u, axis = 1, keepdims=True), u.shape[-1], axis=1)\n",
    "    u_min = np.repeat(np.amin(u, axis = 1, keepdims=True), u.shape[-1], axis=1)\n",
    "    u_range = u_max - u_min\n",
    "    return (u - u_min)/u_range\n",
    "\n",
    "plt.plot(f, normalize(all_spectra)[i_stream, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83bd1c8890>]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vZ5/t/7GRQgWbP4iIyJa8CxWmUhHrUMwlS4CjjwbGjpXbyy93tuN13XXAa6+FL10elIIC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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83bd3869d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(f, np.log(all_spectra[i_stream, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:60: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-166-2af74a7b25c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregularize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_spectra\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi_stream\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;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'"
     ]
    }
   ],
   "source": [
    "regularize(all_spectra)[i_stream, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:60: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83bd87c850>]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9bdO1efPgmmsq+7trpVLR0WGraQoNfwQBrFoV/XmJpJlCRQjKGf4oplKxZIlV\nGV55Jf+GX/kEAfzzn/D979tW2y+9ZO2gtayz+u29t7VPv/ZaeOGF3o8PAuuM2pOODgsItVCpePtt\ne72FQoU/RkQqp8yOA9KTtrbSu072VqlYscKaUS1YYF/X1cF++9lt//3t/h3vsO+9/jo88UTmtmyZ\nffI98EDr2Dh1anmvS6J3+eXw8MM2DPL88/bnmGvDBvjpTy0svvYazJ3bvTtqthUrLHyUWqlYtcqC\nbJK2qS/U+AoUKkTCEvo/Ec65zznnXnfOrXfO/dk5t3/Yzxm3Sk/U3LTJulCuXQtz5sCjj8Ill9ic\nCL/x184723r8nXeGXXeF886zN5hPfMKOX7UKnnrKvpbkGDDAhkFefHHrXV0XLbKN2XbcEc4+OxMU\nvvnNwr+vlG6a3ujRFij8hnJJUYuh4pVX4j4DqXWhViqcc6cB3wHOBZ4HpgOPOud2C4IgZf87Z5QT\nKgYNsjeQ3FARBPD5z1uPiMcfh332scePOSZzzNKltkrgr3+1537f+6ytdrkbf0l1mTIF/uu/4Bvf\ngA9+0KoNt9xim74NG2bb1H/2s9Zu/frr4YorbLnwLrts/btK2ffDy27VPWrUtr+eqPhQsd12W39v\n5EgbAkxTqHjhBdh3XxvunDw57rORWhX28Md04IdBENwD4Jw7H/gA8EnghpCfOzbt7aWv/oD8rbpv\nvhnuuAN+9CMb/shn3Dg44QS7STpdcQX88pc21LVpk3U3/cEP4KyzugfYCy6wvzNXXgn33rv17ym3\nUgEWKiZNKv81RG3JEqtI5GtB36+fBYs0hYr58+1+0SKFColPaMMfzrn+wFTgcf9YEAQB8AfgoLCe\ntxqUU6mArVt1/+531mHxkkvg4x+v3PlJ8gwcCA88YCHi8cdtc7fzz9/679mQIdbj4v77bcgk1/Ll\nNi+jlCpWUjcVW7w4/9CHl7YGWAsX2r0PjiJxCHNORQPQF1iW8/gyoMA0snQoN1TU1WUqFS+9BKef\nbtWHb32rsucnybTXXjbR9ogjel69c845NvTxla9s/b233rKhj1JW/yQ1VCxZkr9HhZe2ULFokd37\nIS6ROFTl6o/p06dTl/NRqqmpiaamppjOqHgdHbZZ17ZUKt5+2zb62nlnuO++rTdDEulJ//42/+KM\nM+CZZ+DggzPfW7689E3hBg604bwkhoo99ij8/bR11VSlorY0NzfT3Nzc7bHWqLe6ziPMUPE20AHk\nTpPaDli85Kb7AAAgAElEQVTa0w/OmDGDKVOmhHVeoVq3zu7LDRVvvw0nn2zzMv74R3W9lPKcdppN\n2rzsMtve3lcmfKWiVEnsVbFkCRx+eOHvNzTkHyJKKh8qVKmoDfk+aLe0tDA15p4BoQ1/BEGwGZgN\nHOkfc865rq+fCet549bWZvflDn889phte/3wwzBhQmXPTWpHnz42bPbUU7ak2CunUgHJCxVBUHjf\nDy+twx+qVEicwu5TcRPwaefcx5xzewC3A0OAH4X8vLFpb7f7cld/ANx5Z/eStUg5jj/eVgxdfnmm\nA2utVCpWrbJVMrUSKlpb7TZsmCoVEq9QQ0UQBA8CFwNXA3OAvYBjgyBIbZbelkrFpz8NP/4xfPSj\nlT0nqU3OWYvvOXPgZz+zx2qlUtFT4yuvocHmMG3aFM05hckPfUyZokqFxCv0jppBENwaBMHOQRAM\nDoLgoCAIQtjMuXpsS6jYZx/42Mcqez5S2w491PZ8ueIKWL8eVq6sjUpFsaECkvW6CvFDH/vtp1Ah\n8UpQJ/9k2JZQIRKGa66x9s033mhf10KlYvFiuy8mVKRhCGThQlv1s+ee1s5/w4a4z0hqlUJFhSlU\nSLXZe29bXuq3UK+VSkV9PQweXPiYtIWKnXbKtCRXtULiolBRYT5UlDNRUyQsV19tPVSg/ErF+vV2\nS4LeVn5AukLFokW2WswHRk3WlLgoVFRYe7vtNZBvi2qRuOy6q00EhvIrFZCcakUxoaKuzvYASUOo\nWLgQ3vGOTGBUpULiolBRYeW26BYJ23XX2c6m5TRUS2OocM6qFWl4A1640CoVPlSoUiFxUaioMIUK\nqVYjRsBJJ5X3s2kMFZCOXhUbN9rrnTDB5pAMG5aOoCTJpFBRYQoVkkZJChXFdNP00hAq/vUvu3/H\nO+x+7FhVKiQ+ChUVplAhaVRXZxvbJSFUrF1rc5t62qHUS0Oo8I2vfFv/MWNUqZD4KFRUWHu7Vn5I\n+jgHo0b1HCpWrYLf/z66cyqkmMZXXhpChW98tdNOdj92rEKFxEehosJUqZC06qlXxWuvwUEHwbRp\n8MIL0Z5XrloLFQsXWn+KQYPs6zFjNPwh8VGoqDCFCkmrQqHimWfgwANt07JRo6C5Ofpzy1ZrocL3\nqPBUqZA4KVRUmEKFpFW+UPHgg3DEETB5Mjz7LJx6qoUKvytqHJYssSHIYpbONjRYQ69168I/r7D4\n5aSeKhUSJ4WKClOokLTKDhVBYH0vTjsNTjkF/u//7PtnnAFvvmnVi7gUu/ID0tFV0ze+8saMsbld\nSQ5KklwKFRWmUCFpNXq0vflu3mzdOS+7DK68Eu69FwYOtGMOOcQmDD7wQHznWUqo8M2ikhoqOjst\nxOUOf4CGQCQeChUVptUfklajR1tZfdo0uOce+PGP4aqrbGWI16cPnH66DYts3hzPeS5eXNxyUshU\nKpL6Brx8uTW/yq1UQHJfkySbQkWFqVIhaTV6NKxZA3/9Kzz2GHzsY/mPO+MMGyb5wx+iPT+vloY/\ncntUgDYVk3gpVFTQpk12U6iQNHrve+G442xC5vvfX/i4vfe2iZtxDYGUEiqGDLHW1kkNFb5HRe5E\nTVClQuKhUFFB7e12r1AhabT77tbcao89ej7OOWhqgl/+MvrJguvXQ2tr8aECkr2sdOFC+/emvj7z\n2MCBts+LQoXEQaGigtra7F6hQmpdU5P9//Cb30T7vKX0qPCSHiomTOg+rwW0rFTio1BRQQoVImbi\nRNh//+iHQGotVOQ2vvLUAEviolBRQX74Q6s/RGzC5u9/b3uCRKXWQkVujwpPlQqJi0JFBalSIZJx\n2mm2rPQXv4juORcvtjkFI0cW/zNJDxX5KhXaqVTi0i/uE4jD+vXWMObNN6186G9vvmmtfU8/HU44\nwWaFl0KhQiRj++3h8MOtbfc550TznH7lR+4cg54kNVSsWQOrVxce/lClQuJQU6Hi5pvhmmu2TvDj\nxlkXwHe8w4LFqadauDj5ZDjzTNvboG/f3n+/QoVId2ecYd03S1nmuS3KeZ4xYyxUBEFpYSRufjlp\noeEPVSokDjUz/PHyy/DlL8ORR1onwD/+EV59FTZssH+Inn8eHnoInnvOjr3oItu/4JhjYMcd4Utf\ngr/8xf7hKcSHiiFDonlNItXu5JOhf3/46U+jeb5yQkVDA2zZYktRkyRf4ytv7FhbzuvneYlEpSZC\nRRDAF78IO+wAd91lnQDf/37YddfMngXZJk2yPQ3mz7cgcfrp9o/iAQfYWv2vfx1eeWXrn2trs0ma\nfWriqor0buRIOP74ymyHvnQpfOUr9mGgkHJDBSRvCGTRIujXL//rVQMsiUtNvP39+tfwyCPw3e+W\nNk/COdhvP5gxA/71L2tNfMgh8J3vwG67Wci4+Wb7xw6074dIPmecYZXAnsJAMaZPh299y7p1fuYz\nNikzVy2FioULrYqab2hWm4pJXFIfKtavt6GL446Dk04q//f07QtHHw133w3LltmGSePH25DKDjvY\nMMnjj2s+hUiuE06w/y9+8pPyf8dTT9nP33YbXHut/f83caLtlOqXrG7aZMGglkJFvqEPyFQqNFlT\nohZaqHDOXe6ce9o51+6cWxnW8/Tm29+2KsPNN1duEtbgwfCRj1gb4qVL4fbb7R+0P/4x8z+ziJgh\nQ+D//T9rhNXTnKRCOjrgC1+wyuC558LFF8OCBTbv6Xvfg112geuus8eg9FAxerTdJy1UFGp8BRr+\nkPiEWanoDzwI3Bbic/TojTfsU81FF9lwRRhGjbLZ7U8+aStHHnoonOcRSbIzzoC5c+Hvfy/9Z++4\nA/72N7jllsx8pbo6+MY3LEh89KPwta9ZB08ofttzb8AA2ysjaaGiUOMrsNdUV6dKhUQvtFARBMHX\ngyC4GXgxrOfozYUX2qeQr3wlmufbcUe7iUh3Rx1l/y+W2rZ75Ur7//fss61SkWu77axaMX8+fOhD\n9v/fO99Z+vklrVfF5s02p6RQpQLUqlvikdo5FY8+Cg8/DDfeqHkOInHr39/6vzQ3w8aNxf/clVfa\nG+i11/Z83DvfCffcY9XCESNKP7+khYp//cuGkgpVKkCtuiUeqQwVmzbZGOz73metgkUkfueea29y\nH/xgcVuiv/gi3HqrDW2MGxfuuTU0JOtTfU89Kjw1wJI4lNRR0zl3LXBpD4cEwOQgCF7elpOaPn06\ndXV13R5ramri3/9u4qmnbDnZsccW7gfx3e/Ca6/Z/IYkdcgTSbN99oHf/c5WYR1/vG2LPnx4/mN9\nb5mJE+0DQtjGjMnfe6Za9dRN0xs7FubMieZ8JHrNzc005zSAaa2CDm6ltum+Ebi7l2MWlHku/zFj\nxgymTJnS7bGWFpuQNW4cTJtmTai+8AVrZJU9vPHvf9sErs9/Hvbcc1vPREQq6YgjrN/L8cfbPItH\nHsm/+dfPf26rqX73O5t0GLaGBnj22fCfp1IWLrQg1FPfHQ1/pFtTUxNNTU3dHmtpaWHq1KkxnZEp\nafgjCIIVQRC83MttS6VPcvNm25Bozz3h9ddh5kz77wsusIlZF19sKz0ALrnElrBddVWlz0JEKuHg\ngy0wvPaabTiW+8a3bp2t2DrhBAsfUUjanIqeelR4fqJmOct4RcoVZp+KnZxzewMTgL7Oub27biX3\nnLzxRhtf/Z//sQlfhx4KP/uZLSc791x7fNddrQHVAw/A9ddDfX3lX5OIVMaUKbYMe9kym/v0739n\nvvftb1tnzJtuiu58GhqsidaWin8kCkdPPSq8MWOs+Z/2/5AohTlR82qgBbgSGNb13y1ASbWZ+fNt\nr42LLrJ/iLJNmAA33GAzoX/wA/uH6fDDbUhERKpbY6N1ymxvh8MOsyrkwoXWyOrCC20Pnqg0NNgn\net+ds9r11KPCU6tuiUOYfSo+EQRB3zy3p4r9HZ2d8KlP2bbkPQ1nDB0K558PL70ETzyhDb1EkmLS\nJBvO7NPHgsU559gci6h6y3hJatUdBMVXKkDzKiRaVf32e/vtMGsW3HlnaRuBiUhyTJhgwaKuzvbP\nuf76wqtCwpKkUPHWW7BhQ3FzKvzxabBuHZx1lg2ZSfWq2lCxaBFceimcd56NuYpIem2/PfzpT3Df\nfXDmmdE/f5JChe9R0dvwh39NaQkVs2fD/ffDM8/EfSbSk1KXlEYiCKwXxYgR9qlFRNKvoSGeQAE2\n5OJcskJFb5WK/v1twnpahj/mzbP7fFveS/WoykrFI4/Y+vTbb7eSqIhImPr2tc0Be/pUv2GDrViJ\ne4nmokU2j2zUqN6PTdP+H3Pn2r1CRXWrylDx7W9be+0TT4z7TESkVowZU7hSsWaNNd07/HB4+ulo\nzyuXX/lRTLfgNDXAUqUiGaoyVASB7TwoIhKVQg2w3n4bjjzSuvqOHg0PPhj9uWUrpvGVl8ZKxZIl\n8Z6H9KwqQ8XFF2dmLouIRCFfqHjzTVvqumiRDX2cdZa1EO/sjOUUATuX3iZpemmpVKxbZ2FqxAhV\nKqpdVYaKadPiPgMRqTW5oeLll6177/r1trR9n33gIx+xN7U4VyDUYqXi5Zetgv3e9ypUVLuqDBXa\nWVREopYdKubMsUAxdKgFCt/d86CDYIcd4hsCaWuDlSuLDxW+UhH35NJt5Yc+jjgCVqyAjRvjPR8p\nrCpDhYhI1HyomDkT3v9+e+N+6inbtNDr0wdOOQUeeiieIZBitjzPNnasvQG3tYV3TlGYNw+22w7e\n/W77eunSeM9HClOoEBHBQsXatbYx4dSp1vLfN5DK9pGP2GTBOFaBFNujwvOtupM+BDJ3LuyxB4wf\nb19rCKR6KVSIiJCZHH7ssdYnp1CrcD8E8rOfRXdu3qJF1lPDv7n2Ji37f8ybB5MnK1QkgUKFiAjW\ng6K52YY2Bg0qfFxYQyCtrXDttT0vmVy40AJNvyJ7Iadh/4+ODpuoOXmydT4dOFChopopVIiIYEHi\n9NOLe8M+9dTKDoEEAXz603D55fCud8Hdd+efXFnKyg+wvhqQ7ErFG2/YvJA99rBJ/Ntvr1BRzRQq\nRERKdOCBlR0CufNO+13//d9w0knwyU/CccfZG2q2YrY8z9a/f+/tx6udX/kxebLdjx+vBljVTKFC\nRKREffrYhM1KDIH885/wxS/CuedateLHP7Y5HXPnQmMjfP/7mefwLbpLkfQGWHPn2tJevwpn/HhV\nKqqZQoWISBkqsQpk/Xrb52iXXWDGjMzjxx8P//gHfOxjcMEF1vTppZfg3/8urVIByW+ANW9eZugD\nNPxR7RQqRETK4IdAtqUR1kUXwauvwk9+AkOGdP/eiBFw663wpz/BsmXW0bOzs/RQkYZKxR57ZL5W\npaK6KVSIiJTBD4H8/Oe2QqFUv/gF3HabVSgaGwsf9973wt//DtOn28TLno7NJ8mViiDILCf1xo+H\nVatsK3qpPgoVIiJlKncIZNEiOOcc+PCH4bzzej9+8GC44Qbr+LnDDqU915gxyQ0Vy5dbgMgNFaDJ\nmtVKoUJEpEwHHmgTCEtZBbJlC5xxhg1v3HFH+HsdjR2b3P0//MqP3OEP0BBItVKoEBEpU3YjrGKH\nQK6+Gv78Z3jgAWvmFLYxY2DTJmtBnjTz5lkH0YkTM49tv73dK1RUJ4UKEZFtcOqptsFVMUMgTz4J\n3/wmXHUVHHJI2Gdmktyqe+5c2HVXGDAg81h9vTUq0/BHdVKoEBHZBu95T3FDIHPmwJlnwvveB5dd\nFs25QbJbdedO0gQbLtIKkOqlUCEisg16GwKZP9+qGVOm2DyK++6zkn5Ukl6pyA0VoFBRzRQqRES2\nUb4hEL/C413vsjkUd94JL75Y+uqNbTV6tH26T1qloq0N3nyz+yRNTw2wqpdChYjINvJDIA8+aBWB\nL30JJk2CX/8abrrJdtk855zidxetpH79krn/x/z5dq9KRbLE8FdcRCRdfCOsO++EH/3IhjeuuML2\n9Bg+PO6zyywrTRK/nHT33bf+njYVq16hVCqccxOcc3c65xY459Y5515xzl3lnOsfxvOJiMTt7LNh\n2DD43Ofg9dfhq1+tjkAByWyANW+ehYe6uq2/N348rF4N69ZFf17Ss7AqFXsADvg08BrQCNwJDAEu\nCek5RURis9de1VuST2qlIt/QB2R6VSxZYktOpXqEUqkIguDRIAjOCYLg8SAI3giC4DfAjcDJYTyf\niIgUltRKRb5JmqCumtUsyoma9cDKCJ9PRERI3k6lW7bAK68UrlRo/4/qFUmocM5NBD4P3B7F84mI\nSIbfqTQp+3+89hps3ly4UjFihG0Vr0pF9SlpToVz7lrg0h4OCYDJQRC8nPUzOwC/B34aBMFdxTzP\n9OnTqcuZndPU1ERTU1MppysiIlilYvNmaG21NtfVbt48uy9UqVBXTWhubqa5ubnbY62trTGdTUap\nEzVvBO7u5ZgF/j+cc+OBJ4BZQRAUscGvmTFjBlOmTCnx1EREJJ/sVt1JCBVz51o1wk/IzKfWG2Dl\n+6Dd0tLC1KlTYzojU1KoCIJgBbCimGO7KhRPAH8BPln6qYmISCX4Vt1vvWVNuaqdn6TZ07bwtV6p\nqFZh9akYDzwJLMSWkI51zm3nnNsujOcTEZHCfKUiKZM1e1pO6qkBVnUKq0/F0cAuXbc3ux5z2JyL\nCLfSERGRUaOSs/9HEFil4kMf6vk4VSqqU1h9Kn4cBEHfnFufIAgUKEREIta3LzQ0JKNSsWQJrFnT\ne6Vi++3tuLa2aM5LiqMNxUREakBSGmD5PT8KLSf11KuiOilUiIjUgKS06p43D/r37739tkJFdVKo\nEBGpAUmqVEya1Ps28WrVXZ0UKkREakBvoWLWLLjwQmhvj+6c8ulpz49sw4fbrrAKFdVFoUJEpAYU\nGv6YNQuOOgoOOwxmzIAnn4z81LopZjmpV+sNsKqRQoWISA3wlQq//0d2mHjrLXjoIfv0/+KL8Z3j\nmjUWEoqpVICWlVYjhQoRkRowdqzt/vmb33QPEz//OcyZAx/+MDQ2wj/+Ed859rbnRy41wKo+ChUi\nIjXAt+o+6aTuYeLkk6FP1ztB3KHCLyfdfffijlelovooVIiI1IApU+CjH80fJrw997Q39i1b4jnH\nefNgp51sAmYxNKei+oTVpltERKrI8OFwzz09H9PYCJs2wauvFj+voZJKmaQJVqloa4O1a+31SfxU\nqRAREcBCBcQ3BFLsclJPDbCqj0KFiIgANu9i7Nh4VoD4CkmplQpIxxDIli1w/PEwc2bcZ7JtFCpE\nROQ/4pqs+b3vQUcH7LNP8T+z/fZ2n4ZQMW8ePPJIZslvUilUiIjIf+y5Z/Sh4o474MtfhssugwMP\nLP7nhg2zuRRpCBUtLXZfSqiqRgoVIiLyH42NNgyxfn00z/eTn8B558HnPgfXXFP6z6dlWens2bDb\nbjBiRNxnsm0UKkRE5D8aG6GzM9OIqhQbNpR2/G9/a8tczzrLhj+cK/0509IAq6XFlv0mnUKFiIj8\nx7veZfelTtZ89VX7lP2hDxUXSJ58Ek45BU48Ee66a+ueGcVKQ6Wis9N6hyhUiIhIqowYARMmlD6v\n4vHH7c3xhRes2vGZz8DSpfmPff55CxOHHQbNzb1vc96TNDTAevll2x126tS4z2TbKVSIiEg35awA\nmTnTPmnPmwff/jb89KcwcSJcdZU1qPJefBGOOw723hsefhgGDty2c/WViiSvmvCTNPfdN97zqASF\nChER6aacFSCzZlnlYeBAmD4dXnvNJl9ed52Fi9tvt8Bx9NFWCfnNb2Do0G0/1/HjYd0666qZVC0t\n8M53wsiRcZ/JtlOoEBGRbhob4c03obW1uOPffBMWLrRQ4Y0cCddfD/PnwzHHwGc/a/M1Ro6Exx6D\n+vrKnGsaGmDNnp2OoQ9QqBARkRyltuueNcvuDzlk6+9NmGB7jsyeDRdcAP/3f5kdUysh6Q2wOjvT\ns/IDtKGYiIjk2H136NvXQkW+oJBr5kzbs6OnsLDvvuHMGUh6qFiwANasUaVCRERSatAgmDSptErF\noYeGe06FDB0KdXXJ7VWRpkmaoFAhIiJ5FDtZc9UqOy57PkXUktyroqUFdtqpskNCcVKoEBGRrTQ2\n2vLP3pZqPv20HRNXpQKSHSrSNEkTFCpERCSPxkZYsQKWLev5uFmz7E39ne+M5rzySWoDrCBI1yRN\nCDFUOOf+1zm30Dm33jm32Dl3j3Nu+7CeT0REKqfYFSAzZ1qVopx9OyolqZWKRYtg5UqFimI9AXwE\n2A04GdgV+FmIzyciIhWy6642YbOnULF+PfzlL/HOp4DMpmJJ66o5e7bdp2n4I7QlpUEQ3Jz15ZvO\nueuAh51zfYMg6AjreUVEZNv17WvNqnoKFX/5C2zeHO98CrBQsX69NeuqVFOtKLS02NDNuHFxn0nl\nRDKnwjk3CjgTeFqBQkQkGXrbA2TWLNuAbM89ozunfJLaq2L27HQNfUDIocI5d51zrg14G9gJ+H9h\nPp+IiFROYyO89JJ1fcxn5kw4+GCrasQpia26gyB9Kz+gxFDhnLvWOdfZw63DObdb1o/cAOwDHA10\nAPdW8NxFRCREjY22w+jChVt/r6MDnnkm/vkUkKlUJKkB1uLF8NZb6atUlDqn4kbg7l6OWeD/IwiC\nlcBK4FXn3DxsbsV7giB4rqdfMH36dOrq6ro91tTURFNTU4mnKyIi5cpeAZK7ZPTFF629dNzzKQAG\nD7aNypJUqfCTNMsNFc3NzTQ3N3d7rLXYHeBCVFKoCIJgBbCizOfyBbKBvR04Y8YMpqQtvomIJMyO\nO1oL7H/8A048sfv3Zs2CAQPggAPiObdcSetV0dJiXTR33LG8n8/3QbulpYWpMY+nhLL6wzl3ALA/\nMAtYBUwErgZeAZ4N4zlFRKSynCs8WXPmTNh/f1t2Wg2K6VWxfr1VNaqBb3oVZ3+PMIQ1UXMd1pvi\nD8A84A7gBeD9QRBsDuk5RUSkwvKFiiCIdxOxfHoKFZ2dcOedsPPO1la8GqRx5QeEFCqCIPhHEARH\nBkEwJgiCIUEQ7BoEweeDIEjQNBoREWlshLlzrR+F9/rr9gZeDZM0Pd8AK9ef/wzveQ98+tNw7LHx\nthP3li6165e2lR+gvT9ERKQHjY0WKF55JfPYrFlWtj/44PjOK5evVPiumkuXwtlnw0EH2UqVWbPg\nnnsyy0/j5Lc7V6VCRERqSr49QGbOtMdHjoznnPLZfnvYuNE2QPvOd2C33eA3v4Ef/tA6fx5ySNxn\nmNHSYtdu553jPpPKU6gQEZGCGhqsjXR2qKi2+RSQqUBMnQqXXAIf/7hVV849N/7mXLnSOkkTFCpE\nRKQX2ZM133oL5s2rrvkUALvsAv37w6RJMGcO3HJLdVVSsqV1kiaEuKGYiIikQ2OjDSWAVSmg+ioV\n48bZnIrRo6u7AvD227bleRonaYIqFSIi0ovGRnjtNVi3zkLFhAmw005xn9XWGhqqO1BAuidpgkKF\niIj0orHRVlXMnWuTNKutSpEkLS0wfDjsumvcZxIOhQoREenRu99t9889Z2+K1TafIkn8JM0+KX33\nTenLEhGRShk2zJpG3Xmn9XxQpaJ8aZ6kCQoVIiJShMZGW1UxahRMnhz32STTqlWwYIFChYiI1Djf\nBOvQQ9Nbug/bCy/YfVpXfoBChYiIFCE7VEh5Zs+GIUOs22daKVSIiEivDjjAmksdfXTcZ5JcLS2w\nzz7V1+GzktT8SkREejVxIqxYYcshpTyzZ9tOqWmmSoWIiBRFgaJ8a9bAyy+ne5ImKFSIiIiEzjm4\n7TY4/PC4zyRcGv4QEREJ2fDhcP75cZ9F+FSpEBERkYpQqBAREZGKUKgQERGRilCoEBERkYpQqBAR\nEZGKUKgQERGRilCoEBERkYpQqBAREZGKUKgQERGRilCoEBERkYpQqBCam5vjPoWao2sePV3z6Oma\n157QQ4VzboBz7gXnXKdzbq+wn09Kp//xo6drHj1d8+jpmteeKCoVNwD/AoIInktERERiEmqocM4d\nDxwNXAy4MJ9LRERE4hXa1ufOue2A/wZOAtaH9TwiIiJSHUILFcDdwK1BEMxxzk0o8mcGAcydOze8\ns5KttLa20tLSEvdp1BRd8+jpmkdP1zxaWe+dg+I6BxcExU91cM5dC1zawyEBMBk4DjgFeH8QBJ3O\nuZ2BBcA+QRD8vYfffwZwf9EnJCIiIrnODILggTieuNRQMRoY3cthrwMPAifkPN4X2ALcHwTBJ3r4\n/ccCbwAbij4xERERGQTsDDwaBMGKOE6gpFBR9C91bkdgRNZD44FHgQ8DzwdBsLjiTyoiIiKxCmVO\nRRAE/8r+2jnXjq3+WKBAISIikk5RdtRUnwoREZEUC2X4Q0RERGqP9v4QERGRilCoEBERkYqomlDh\nnPucc+5159x659yfnXP7x31OSeCcu8w597xzbo1zbplz7mHn3G55jrvaObfYObfOOfd/zrmJOd8f\n6Jz7gXPubefcWufcQ865sTnHjHTO3e+ca3XOrXLO3emcGxr2a6x2zrn/6tow76acx3XNK8g5N945\nd2/X9VrnnPubc25KzjG65hXinOvjnPuGc25B1/V81Tn31TzH6ZqXyTl3mHPuV865f3f9G3JSnmMi\nub7OuZ2cc791zrU755Y6525wzpWeEYIgiP0GnIb1pfgYsAfwQ2Al0BD3uVX7Dfgd8FGs6diewG+w\nPh+Ds465tOt6ngA0Ar8EXgMGZB1zW9fPvQ/YF3gGmJnzXL8HWoD9gIOBl4H74r4GMV///bHGbnOA\nm3TNQ7vO9VgPnDuBqcAE4CjgnbrmoV3zy4HlWDPDdwAnA2uAz+uaV+waHwdcDXwQ6ABOyvl+JNcX\nKzC8iLV+2BPrF7Uc+GbJrynui9r1gv4M3Jz1tcN2Nr0k7nNL2g1oADqBQ7MeWwxMz/p6BLYfy6lZ\nX28EPpR1zO5dv+eArq8nd329b9Yxx2INzcbF/bpjutbDgPnAEcAf6R4qdM0re62vA/7UyzG65pW9\n5kBhGB4AAAQMSURBVL8G7sh57CHgHl3zUK53J1uHikiuL3A8sJmsD/LAecAqoF8pryP24Q/nXH/s\nk8fj/rHAXtEfgIPiOq8Eq8eW764EcM69ExhH9+u7BniOzPXdD+tZkn3MfGBR1jEHAquCIJiT9Vx/\n6Hqu94TxQhLgB8CvgyB4IvtBXfNQnAj81Tn3YNcwX4tz7lP+m7rmoXgGONI5NwnAObc3cAhWHdU1\nD1nE1/dA4MUgCN7OOuZRoA54dynnHeaGYsVqwFp4L8t5fBmWuKRIzjkHfBeYFQTBP7seHof95cl3\nfcd1/fd2wKauv7CFjhmHlcP+IwiCDufcyqxjaoZz7nRgH+x/6ly65pW3C/AZ4DvANcABwPeccxuD\nILgXXfMwXId9Ep7nnOvASuRfCYLgJ13f1zUPV5TXd1yB5/Hf+1uxJ10NoUIq51bgXdinCQmJszb0\n3wWOCoJgc9znUyP6YC3+r+j6+m/OuUbgfODe+E4r1U4DzgBOB/6JheibnXOLu4KcyFZiH/4A3sYm\nqGyX8/h2wNLoTyeZnHPfB6ZhO8MuyfrWUmyOSk/XdykwwDk3opdjcmcU9wVGUXt/TlOBMUCLc26z\nc24zNknqi865TVjC1zWvrCXA3JzH5mITCEF/z8NwA3BdEAQ/C4LgpSAI7gdmAJd1fV/XPFxRXt+l\nBZ4HSvwziD1UdH3Smw0c6R/rKuMfiY3pSS+6AsUHgcODIFiU/b0gCF7H/lJkX98R2Fiav76zsUk7\n2cfsjv2D/WzXQ88C9c65fbN+/ZHYX/rnKvl6EuAP2AzpfYC9u25/Be4D9g6CYAG65pX2NFsPh+4O\nLAT9PQ/JEOwDX7ZOut43dM3DFfH1fRbY0znXkHXMMUArVqUq6cRjvwGnAuvovqR0BTAm7nOr9hs2\n5LEKOAxLlv42KOuYS7qu54nYm+EvgVfovizpVmzJ3vuxT+JPs/WypN9hb577Y0Ms84F7474G1XBj\n69UfuuaVvb77YbPcLwN2xcrya4HTdc1Du+Z3YxP+pmFLeD+Ejc1/S9e8Ytd4KPahZB8ssH2p6+ud\nory+WFD8G7b0dC9sdcgy4Bslv6a4L2rWi/osttZ2PZaa9ov7nJJw6/qL2JHn9rGc467Clietw2b1\nTsz5/kDgFmw4ai3wM2BszjH12KfxVizI3AEMifsaVMMNeIKsUKFrHso1ngb8vet6vgR8Ms8xuuaV\nu95DgZu63rDau97Mvk7OEkNd8226xu8r8G/4XVFfX2AnrM9RGxYorgf6lPqatKGYiIiIVETscypE\nREQkHRQqREREpCIUKkRERKQiFCpERESkIhQqREREpCIUKkRERKQiFCpERESkIhQqREREpCIUKkRE\nRKQiFCpERESkIhQqREREpCL+P31GSp4YIrqJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83bddc9910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(f, regularize(all_spectra)[i_stream, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83be30f750>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OCin3UJFN04ckVVZKAwcGGyok61dB8wcAIGphVyo+L2mQpMclrUt7zA3rhH6fhTiGilyX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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83be383210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yy = all_spectra[:50]\n",
    "y_thresh = np.repeat(np.amax(yy, axis = 1, keepdims=True), yy.shape[-1], axis=1)/np.exp(90/10)\n",
    "yy[yy<y_thresh]=0\n",
    "plt.plot(f, np.log(yy[20,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83bead1110>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S2jXYvgi4A7gFOBw4H3glcNZEfoOaujIUYsVCktRGdSsWb6NaBXLNyP7zgE8Ofj4a2Dl0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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83beb4a7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4445f976d0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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15D//M2+PKsOGZyOVJDUybHzve53H7bN9dnv00S1bz+67w667dp63T7rVNm1a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\nGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4468456050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = np.arange(y.shape[1])*p_s.pars['sys']['s_f']/y.shape[-1]/2\n",
    "plt.plot(f, y[i_stream,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'f' 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-8-5df16955bf0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msynthetic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\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[0;36m2048\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspectral_slice_tf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'f' is not defined"
     ]
    }
   ],
   "source": [
    "i=1000\n",
    "print(i)\n",
    "x = p_s.synthetic(i).flatten()[:2048]\n",
    "s = spectral_slice_tf(x)\n",
    "plt.plot(f, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_s.ab.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f061c7b7a10>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dxfcbgK6ImNVS3egptu3VwMAAc+fO3ZchS5I0Ze3pD/ChoSF6e3s7NKJsn66zERHPIAeN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f062c471410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = spectral_slice_tf(x)\n",
    "f = np.arange(s.size)*p_s.pars['sys']['s_f']/s.size/2\n",
    "plt.plot(f, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44100.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_s.pars['sys']['s_f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.666666666666667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00064711,  0.00051946,  0.00043862, ...,  0.00014497,\n",
       "        0.00014609,  0.00014595], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-10-28 00:53:07,897 h5tools      DEBUG    Translating group sweep_pars into its own dictionary\n",
      "2016-10-28 00:53:07,899 h5tools      DEBUG    Translating group sys into its own dictionary\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'sys', <HDF5 group \"/sweep_000/vocal_pars/sys\" (0 members)>)]\n",
      "/sweep_000/synth/23500\n",
      "[(u'alpha_beta', <HDF5 dataset \"alpha_beta\": shape (159500, 2), type \"<f8\">), (u'pre_in', <HDF5 dataset \"pre_in\": shape (159500, 4410), type \"<f8\">), (u'pre_out', <HDF5 dataset \"pre_out\": shape (159500, 4410), type \"<f8\">)]\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File(sweep_file_path, 'r')\n",
    "p_s = ParSweep(f, sweep_name)\n",
    "#one_stream = p_s.synthetic(0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2205)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def slice_compare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5tools as h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'h5tools.tables' from '/mnt/cube/earneodo/repos/swissknife/h5tools/tables.pyc'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5.tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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   "cell_type": "code",
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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