eval_synthesized.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make sweeps in the parameter space of the Mindlin model and create synthetic vocalizations\n",
    "\n",
    "Uses a RK4 ode integrator to compute segments of synthetic vocaliations of zebra finch (after the Mindlin Model presented in Sanz, Arneodo, Amador, Mindlin 2011 and Arneodo, Sanz, Goller, Mindlin (2012).\n",
    "\n",
    "Requires package swissknife (https://github.com/zekearneodo/syrinxsynth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-11-25 22:34:52,316 matplotlib.backends DEBUG    backend module://ipykernel.pylab.backend_inline version unknown\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import logging\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.signal import spectrogram\n",
    "import scipy.signal as sg\n",
    "\n",
    "# Import the package that generates synthetic vocalizations\n",
    "from syrinxsynth.dynamo import finch as zf\n",
    "from syrinxsynth.dynamo import takens as tk\n",
    "\n",
    "# Set up a logger\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a grid of parameter values (alpha, beta) in the phonation region\n",
    "There is a region delimited by a cusp bifurcation and a hopf bifurcation where stable oscilations occur.\n",
    "Method dynamo.takens.cusp_grid() yields a grid of vaules for the parameters alpha, beta that lies within that region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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_grid = tk.cusp_grid(b_max=0.07, b_min=-1/3 , a_step=5E-2, b_steps=50, b_log_step_exp=5, f_max=None);\n",
    "plt.plot(lines[:,1], lines[:,0], label='+cusp line');\n",
    "plt.plot(lines[:,2], lines[:,0], label='-cusp line');\n",
    "plt.plot(ab_grid[:,0], ab_grid[:,1], '.', label='phonation');\n",
    "plt.xlabel('alpha');\n",
    "plt.ylabel('beta');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Plot above, every green point is in the region of phonation (i.e, leads to an oscilatory solution).\n",
    "It is below the SNIC bifurcation line, and left to the Hopf bifurcation line.\n",
    "Integrating the model for any pair of (alpha, beta) in that group, leads to a synthetic song segment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's pick some of those points and make synthetic sounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick a sample of those points:\n",
    "alpha_beta_grid = ab_grid[ab_grid[:,0]<-0.1][::1]\n",
    "alpha_beta_grid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'syrinxsynth.dynamo.finch' from '/Users/zeke/repos/syrinxsynth/syrinxsynth/dynamo/finch.py'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "reload(zf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from swissknife.dynamo import finch as zf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-11-26 00:40:21,712 root         INFO     Simulating 0/150 with 0, 150\n",
      "2018-11-26 00:40:21,712 root         INFO     Simulating 0/150 with 0, 150\n",
      "2018-11-26 00:40:45,224 root         INFO     Simulating 10/150 with 10, 150\n",
      "2018-11-26 00:40:45,224 root         INFO     Simulating 10/150 with 10, 150\n",
      "2018-11-26 00:41:08,500 root         INFO     Simulating 20/150 with 20, 150\n",
      "2018-11-26 00:41:08,500 root         INFO     Simulating 20/150 with 20, 150\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-120-a0051e8218c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mpar_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0malpha_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_values\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[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;31m# Integrate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0msong_synth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocal_pars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpar_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msim_pars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x_0'\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     32\u001b[0m     \u001b[0msynth_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msong_synth\u001b[0m\u001b[0;34m[\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[0m\n",
      "\u001b[0;32m~/repos/swissknife/swissknife/dynamo/finch.py\u001b[0m in \u001b[0;36mfinch\u001b[0;34m(pars, int_par_stream, x_0)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0msyrinx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_pars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sys'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msyrinx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\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    160\u001b[0m         \u001b[0msampling_t\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/repos/swissknife/swissknife/dynamo/ode.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mode_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvector_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_pars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/repos/swissknife/swissknife/dynamo/ode.py\u001b[0m in \u001b[0;36mrk4\u001b[0;34m(f, x, h, *args)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mk_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mk_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mk_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mk_4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk_1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2.\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk_2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk_3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk_4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m6.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sweep_name = 'sparse_grid'\n",
    "sim_pars = {'sweep_name' : sweep_name, #just for keeping track of the simulation series\n",
    "            'gamma': 23500,\n",
    "            'alpha': 0.,\n",
    "            'beta': 0.,\n",
    "            'vocal_pars': vocal_pars, #parameters of the vocal organ\n",
    "            'sim_samples': 3000, #length of vocalization for each parameter set\n",
    "            's_f': 30000., #sampling frequency\n",
    "            'steps_per_sample': 20, # integration step\n",
    "            'x_0': np.array([5.E-12, 1.E-11, 1.E-12, 1.E-11, 1.E-12])\n",
    "           }\n",
    "\n",
    "#allocate memory for the array of simmulated sounds\n",
    "total_sim = alpha_beta_grid.shape[0]\n",
    "synth_table = np.zeros([total_sim, sim_pars['sim_samples']])\n",
    "\n",
    "#run the integration for the grid\n",
    "for i, (sim_pars['alpha'], sim_pars['beta']) in enumerate(np.nditer([alpha_beta_grid[:,0], alpha_beta_grid[:,1]])):\n",
    "    if i%10==0:\n",
    "        logger.info('Simulating {0}/{1} with {0}, {1}'.format(i,\n",
    "                                                          total_sim, \n",
    "                                                          sim_pars['alpha'],\n",
    "                                                          sim_pars['beta']))\n",
    "        \n",
    "    # Make streams of alpha, beta, env parameters for synthesis of song segments\n",
    "    alpha_values = sim_pars['alpha'] * np.ones(sim_pars['sim_samples'])\n",
    "    beta_values = sim_pars['beta'] * np.ones(sim_pars['sim_samples'])\n",
    "    env_values = np.ones(sim_pars['sim_samples'])\n",
    "    par_stream = np.array([alpha_values, beta_values, env_values]).T\n",
    "    # Integrate\n",
    "    song_synth = zf2.finch(vocal_pars, par_stream, x_0=sim_pars['x_0'])\n",
    "    synth_table[i,:] = song_synth[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-123-4cf9a9bc1b04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"zf.finch(vocal_pars, par_stream, x_0=sim_pars['x_0'])\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/mountain/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2129\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2130\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2131\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2132\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-61>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mountain/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mountain/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1095\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\u001b[0m                 \u001b[0mnumber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1097\u001b[0;31m                 \u001b[0mtime_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1098\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtime_number\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mountain/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
      "\u001b[0;32m~/repos/syrinxsynth/syrinxsynth/dynamo/finch.py\u001b[0m in \u001b[0;36mfinch\u001b[0;34m(pars, int_par_stream, x_0)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0msampling_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msteps_per_sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sys'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'noise'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\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[0m\u001b[1;32m    153\u001b[0m         \u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'A_1'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sys'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'envelope'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m                       (1. + pars['sys']['noise_fraction_env'] *\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "reload(zf)\n",
    "%timeit zf.finch(vocal_pars, par_stream, x_0=sim_pars['x_0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.42 s ± 121 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reload(zf2)\n",
    "%timeit zf2.finch(vocal_pars, par_stream, x_0=sim_pars['x_0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Now there are synthetic song segments in synth_table, corresponding to the parameter pairs in alpha_beta_grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.11851324, -0.04525277])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_beta_grid[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c1c3a28d0>]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_synth = synth_table[10]\n",
    "plt.plot(one_synth[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 129)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# functions using tensorflow\n",
    "def real_fft(x, only_abs=True, logarithmic=False, window=None):\n",
    "    \"\"\"\n",
    "    Computes fft of a stack of time series.\n",
    "    :param x: ndarray, shape=(n_series, n_samples)\n",
    "            Input stack of time series\n",
    "    :param only_abs: boolean \n",
    "            return only absolute values (power spectra)\n",
    "    :param logarithmic: boolean\n",
    "            return in logarithmic scale; ignored if only_abs==False\n",
    "    :param window: ndarray, shape=(n_samples, )\n",
    "            boxing window (default none), np.array (n_samples)\n",
    "    :return: ndarray, shape=(n_series, n_samples/2) \n",
    "            with fft computed for every row\n",
    "             dtype=np.float32 (if only_abs==True) or dtype=np.complex64 (if only_abs==False)\n",
    "    \"\"\"\n",
    "    n_series, n_samples = x.shape\n",
    "\n",
    "    tensor_x = tf.Variable(x, dtype=tf.float32)\n",
    "    if window is not None:\n",
    "        assert(window.size == n_samples), 'Window must be size n_samples'\n",
    "        vector_win = tf.Variable(window.flatten(), dtype=tf.float32)\n",
    "        tensor_win = tf.reshape(tf.tile(vector_win, [n_series]), [n_series, n_samples])\n",
    "        real_x = tf.multiply(tensor_x, tensor_win)\n",
    "    else:\n",
    "        real_x = tensor_x\n",
    "    img_x = tf.Variable(np.zeros_like(x), dtype=tf.float32)\n",
    "    complex_x = tf.complex(real_x, img_x)\n",
    "    complex_y = tf.fft(complex_x)[:, :int(x.shape[-1]/2)]\n",
    "\n",
    "    if only_abs:\n",
    "        amps_y = tf.abs(complex_y)\n",
    "        if logarithmic:\n",
    "            log_10 = tf.constant(np.log(10), dtype=tf.float32)\n",
    "            fft = tf.multiply(tf.log(amps_y), log_10)\n",
    "        else:\n",
    "            fft = amps_y\n",
    "    else:\n",
    "        fft = complex_y\n",
    "\n",
    "    model = tf.global_variables_initializer()\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(model)\n",
    "        s = sess.run(fft)\n",
    "    return s\n",
    "\n",
    "i=0\n",
    "rows_chunk = 1000\n",
    "n_samples = 129 * 2\n",
    "x_i = p_s.synthetic(i*rows_chunk, rows=rows_chunk)[:, :n_samples]\n",
    "rft = sp.real_fft(x_i, only_abs=True, logarithmic=True, window=np.ones([1, 129*2]))\n",
    "rft.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('complex64')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rft.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0c937c8a90>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kABgAEBFRLsuMACA0A2CzxzUOABC+CSAwA2ClF4CxDh4/mwCIiKhlyYwAIHQkwBhNAJGe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c937c8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser=0\n",
    "plt.plot(x_i[ser])\n",
    "plt.plot(np.abs(rft[ser]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 160/160 [01:25<00:00,  1.01s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "n_streams = p_s.ab.shape[0]\n",
    "f_bins = 129\n",
    "n_samples = f_bins * 2 # resolution of fft is given by n of samples\n",
    "\n",
    "all_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",
    "    all_spectra[i*rows_chunk:(i+1)*rows_chunk, :] = normalize(regularize(spectral_slice_tf(x_i)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 258)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_i.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2048)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streams = p_s.synthetic(0, rows=5)[:,:2048]\n",
    "streams.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0d1c1776d0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4806or4clS+CrX1XRdc018MYb8Ne/pm2hUSxpzPBaWmDjxmTuVyzOwZQp8J73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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d5a4e0c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i_stream = 1\n",
    "t_steps = 100\n",
    "t = np.arange(x_i.shape[1])/p_s.pars['sys']['s_f']\n",
    "plt.plot(t[:t_steps]*1000, x_i[i_stream, :t_steps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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",
    "syll_data.to_pickle(os.path.join(sweep_folder, 'spectral_pandas_{}.pickle'.format(sweep_name)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/usr/local/experiment/tmp_data/syn_sweeps/spectral_pandas_sweep_001.pickle'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(sweep_folder, 'spectral_pandas_{}.pickle'.format(sweep_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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",
       "      <th>bird</th>\n",
       "      <th>sess</th>\n",
       "      <th>spectral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.139136</td>\n",
       "      <td>synth</td>\n",
       "      <td>sweep_002</td>\n",
       "      <td>[0.29575958848, 0.286303639412, 0.291149049997...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.139588</td>\n",
       "      <td>synth</td>\n",
       "      <td>sweep_002</td>\n",
       "      <td>[0.486470043659, 0.435143560171, 0.41754218936...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.140047</td>\n",
       "      <td>synth</td>\n",
       "      <td>sweep_002</td>\n",
       "      <td>[0.421198099852, 0.448597162962, 0.45280805230...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a         b   bird       sess  \\\n",
       "0 -0.08 -0.139136  synth  sweep_002   \n",
       "1 -0.08 -0.139588  synth  sweep_002   \n",
       "2 -0.08 -0.140047  synth  sweep_002   \n",
       "\n",
       "                                            spectral  \n",
       "0  [0.29575958848, 0.286303639412, 0.291149049997...  \n",
       "1  [0.486470043659, 0.435143560171, 0.41754218936...  \n",
       "2  [0.421198099852, 0.448597162962, 0.45280805230...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syll_data[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_s.pars['sys']['s_f']"
   ]
  },
  {
   "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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iUjwVFyzmzYOvfhUGDoRDD838fa0JFlEXRq4Vi44dYaONcgsWLVUsQMFCRESKJ6dt0+Nq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\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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\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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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": [
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