manual_pitch-z028.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computer: passaro\n"
     ]
    }
   ],
   "source": [
    "# Definitions and functions\n",
    "%matplotlib inline\n",
    "import pdb\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import scipy.signal as sg\n",
    "import math\n",
    "import scipy as sp\n",
    "import socket\n",
    "import os\n",
    "import wave\n",
    "import struct\n",
    "import h5py\n",
    "from scipy.io import wavfile\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.signal import spectrogram\n",
    "import logging\n",
    "from IPython.display import display\n",
    "matplotlib.style.use('ggplot')\n",
    "from ipywidgets import *\n",
    "import yaml\n",
    "import numpy.lib.recfunctions as rfn \n",
    "import numpy.matlib as npm\n",
    "import glob\n",
    "from __future__ import division\n",
    "# Check wich computer to decide where the things are mounted\n",
    "comp_name=socket.gethostname()\n",
    "print '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",
    "if 'lookfar' in comp_name:\n",
    "    repos_folder = os.path.abspath('/Users/zeke/repos/')\n",
    "    experiment_folder = os.path.abspath('/Users/zeke/experiments/bci_zf/')\n",
    "\n",
    "sys.path.append(os.path.join(repos_folder, 'soundflow', 'sound_tools'))\n",
    "sys.path.append(os.path.join(repos_folder, 'ephysflow'))\n",
    "sys.path.append(os.path.join(repos_folder, 'analysis-tools'))\n",
    "\n",
    "#setup the data folder\n",
    "ss_data_folder = os.path.join(experiment_folder, 'ss_data')\n",
    "\n",
    "# from file_tools import experiment as et\n",
    "# from basic_viewing import events as evt\n",
    "# from basic_viewing.units import Unit\n",
    "# from basic_viewing.basic_plot import plot_raster, sparse_raster\n",
    "# from basic_viewing import basic_plot as bp\n",
    "# from basic_viewing import kwik_functions as kwf\n",
    "# from basic_viewing import h5_functions as h5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bird = 'z028'\n",
    "stim_sess = '001'\n",
    "base_name = 'motiff_2b'\n",
    "\n",
    "stim_folder = os.path.join(experiment_folder, 'stim_data', bird, stim_sess)\n",
    "pitch_file = os.path.join(stim_folder, 'pitch_list - {0}.tsv'.format(bird))\n",
    "bos_file = os.path.join(stim_folder, '{}.wav'.format(base_name))\n",
    "auto_pitch_file = os.path.join(stim_folder, 'f0_smooth_{}.dat'.format(base_name))\n",
    "env_file = os.path.join(stim_folder, 'env_{}.dat'.format(base_name))\n",
    "#out_pitch_file = os.path.join(stim_folder, 'f0_extern_bos.dat')\n",
    "out_pitch_file = os.path.abspath('/mnt/cube/earneodo/repos/synthesizer_working/f0_extern_{}.dat'.format(base_name))\n",
    "\n",
    "\n",
    "auto_pitch_list = np.loadtxt(auto_pitch_file)\n",
    "pitch_list = np.loadtxt(pitch_file)\n",
    "env_list = np.loadtxt(env_file)\n",
    "s_f, bos = wavfile.read(bos_file)\n",
    "\n",
    "t= np.arange(0., np.max(pitch_list[:,0])*s_f+1)/s_f\n",
    "\n",
    "t_out = np.arange(0., np.max(pitch_list[:,0])*s_f+1)/44100.\n",
    "\n",
    "interp_pitch = np.interp(t, pitch_list[:,0], pitch_list[:,1])\n",
    "pitch_f = interp1d(pitch_list[:,0], pitch_list[:,1], kind='cubic')\n",
    "#interp_pitch = pitch_f(t)\n",
    "out_pitch = np.array([np.arange(t.size), t, interp_pitch]).T\n",
    "np.savetxt(out_pitch_file, out_pitch, fmt='%lg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcedbdc0910>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dyJIeRR7AZj+BrE9BeGdEiKgIFxUI0gWhCYxTdLIeUOjubcFsmsimX9anLHt4OE6ebOeJ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\n/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcedbf05f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, bos[:t.size]/10)\n",
    "plt.plot(pitch_list[:,0], pitch_list[:,1], 'k')\n",
    "plt.plot(env_list[:,0], env_list[:,2]*1000)\n",
    "plt.plot(t, interp_pitch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interp_pitch = np.interp(t, pitch_list[:,0], pitch_list[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.97569161])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44100.        ,  44100.        ,  44100.        , ...,\n",
       "        44099.99999998,  44099.99999998,  44099.99999998])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/np.diff(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   2.26757370e-05,   4.53514739e-05, ...,\n",
       "         9.75623583e-01,   9.75646259e-01,   9.75668934e-01])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0., pitch_list[-1,0]*s_f)/s_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcedb57e590>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZgKvd/wMqkJB8THWQ0v/BYvW1jm6yQ+LiXNte+vsqftiysdfjJvUSICINBpot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\nIYSGSRAQQggN+//N01NPtGoUqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcedb57e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, interp_pitch)\n",
    "plt.plot(pitch_list[:,0], pitch_list[:,1], 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcedbe00690>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3243D4cDtdjN7tvVU6nQ66enpCb/O5XLhdDpHbe/p6cHpjP6hr6uro66uLvxz\nd3dyffjLysrG3FebJrr/Ev1aMdjdjdZWEDt/6iRqdumo18cjcMFj9d3vOk3v8SNc7F6V1HFiMc9Y\nDc8urx8VcW0B08Q/MBB3XpWVleE5Y/Vk8vgCw441ofRp0P2XwukIuHsILFo8RroU6n0fZLDlpwz+\nrg316b/GGKd9IOD3E/B6474+nZNLf28vgwleV7T3jEhv3pimycDgAN4UHl8vvIKBoy/hm4S/Zya+\nbxYsWBDX65KqDqqpqaGtrQ2AtrY2Vq5cGd7e3t6Oz+ejq6uLM2fOsGTJEhwOB0VFRRw9ehStNQcP\nHqSmpiaZU6dGqM66KNgWEFxdTAfr25PicVvVHmXzE6viSFSvB2bMRI2cO8jISbhNQF8KTt8wY2aK\nEofVJuDzon3B2Rx7L8CsOWO+1PjYBoz7/g5y8uDYK6lLA0BePvqlQ5hP/hx9vif268XUS3GNgnr7\nFfDmSWuurQygBweGPnMZJGZJ4OGHH+bll1+mt7eXz33uc9x2222sW7eOhoYGWltbw11EARYuXMjq\n1avZunUrhmGwYcMGjODc8xs3bmTv3r14vV6qqqqmtmdQf8TkcTBUv96XXBDQl/rg9U7ULR9Bnzxu\njWxNl74Lo7uHQrCLaKINw8E35MwUtgm8owIN6PbfoK5abaVp9hjpDb0+tJ5DPHXCCdwj1K0fQ//H\nT62BaT/cj/HFnajgKnIiA6VhBly1aDE64IfTJ+DtS1J+/BCttdUb6eRxON+DdvfA+R5wD/8+NF+Z\nse1bGfVejBkENm/ePOb27du3j7l9/fr1rF+/ftT2xYsXh8cZTDl/xMyDYD0JK2Oo+2WiXnketIla\nVg0D/eijL6K1Tss4CN3rGd0oDMn1DrrUZz19hYJhKiy/Cpa8C/34v6AWXWFtG9k7aCyx7gFaoxKI\nAsbam2HtzejOP2B+84voN14b9sHTp0/C7Dmo4jHyUkyBBLoAxyuicVglGQS032+V8s/3gLvbKlW6\nXRHf98B5F+dGdkJQBpTMAUcZXLYAdeW7oa8X/d9t1rK2GcSeI4ZDN5zge04ZOdbTcLIlgZcPW/3j\nr3in1SVtcMDqHx8aRZxKvR6YO3/09mTGCVzqg6KZVm+aFFFKYaz/NOa3tmF+425r28h5jkbvROwo\nkKTLl1p5E1ElpH0+zB13Wqe+bQPqhluSHx8iUifVz0xz51ujiOPoIaQvXUQ//Ws491bEzb3H+hyP\nLKXk5cO7eEfwAAAS0UlEQVQcJzjKUFdcCY5Siv9oERfzC2FOqfWvxIEaMceWPvWaFQQmYdmPRNg0\nCIyIAgCzShJqE9CDA+iDv0Itq7aCwDtXWPX0c99m/Y3fOp2eINB3YWhd5EjJjhNIYVVQiKpYhlp5\nHfr1VzFu/yvUH70jxg4xpq8Gku1HrnJyrCeyyHaBnqH1pPWPDqDbfonxyU2oZVUJH1+kSBpujEop\nq1T6h8Pjlsy134+5+6twotMaM+MoBUcpauHl1vdzSlGOMnA4rRv8zFmjjjWjrIxLMRuGQ/tkVhSw\nZxCInDs/ZPYc6H4r/iP85B/Rrf9vqFBx44esby6zWuT12VOjJ3lLhb4LUDxr9HalrMndEqAvuIcN\nFEsl9dm7URBnlVgcr9Ek33BY4kSfjyiCB//Oxj3fgMEBzH89gPno/Rjf+C5kaTe/rJfIiPAEqOpV\n6H98FP2bn6FzcjHGmL1W/+ZxONGJ+st7MFZel/I0DCUmMyezs+eiMmMEYlV5NZw8hj4bewI4ffwI\n+smfo669CbX+07CsGnX1GuuXpfPAUYZ+4ZkUJzo4108gMHpBGbBGPXtc1mviEDjvgqMvoq6sTHEq\nLQmPH0nn0piOUjj7Jjq4ZoQOBfuy+ajKGowv7LCmDfnVT9KXBhFDakcMh6iqa6yj/+sBq5PA6ZPD\nzzo4iH78h/DH70lvALBSEzxpZpUE7BkExigJqGuuB2VY00GPt6cZwPzHR60i4sc2YPzJn5Oz5evh\nxUuUYVi9Yl48ZM1UmtJkj1GNFdry7ho4dxbeeC2uQw0c/DWYJmp18nO1pIwi9gdjAh8cdfV7wdWN\n+bW/Rr/wrFUSyM216nUBNXc+atUN6LZfEnBnX3dSPThA4Mt/iX7l+alOysSkIwjMKoEVV4d/1r95\nfPgLLrhhcABVvTrl544mw2KATYOAHiMIzHHCiqvQ//WkNV1zNM93wJsnUB+5Y2jaiRHU1e8Fvw/9\nfKpLA2NUY4XOedUayMlB//fBuI408OQv4B0VqPJFqUxgkuL58Cf/pGisuh7jS9+CwiLMPV+3nvhH\nNIirP/0oBPxcav6npM4xpbq74NxZzAMNU52S5KXxxmh8bhvG3n9Hrb3Z+nxHtv0Fu22qohT2kIsm\n/PbNrChg0yAQ+mb4TcVY8z6r69fvo99IzSefsEoBV783+vEXX2nVQz/bPvG0RopeELC6Oi6rRnf8\nZ8w5hPTJ4/hffxU1gRkbUyru3kHJPymqy5difPVh1C3BuYVHdBlU8xagrqnl0i+bJjZocCqEps7O\n6kFx6bsxqoICVF4e6sYPgs+L+fe7h9a5CI0ZSuWAyagJkeqgDGL9EUY9WFZdY/Um+Me/Rf/hf0bv\ndfZNePkwqvbmUd2/IllVQqvgxWfQE12nYFgCxokCgFp5HbjOwasvjX+Y9t9Abh7qPemuA41TPDEg\nBR8clZeH8eFPYTywD+PTfz369zf8KXgH0a+M/ttni3jbhDKOTk+bQCS1YBHqLz4Hr7yA+TebrbU/\nQmuLT0YQyNA2AXv2DhqjOghA5eZh3PlVzIe+hNn4DYwtXx8+wOipJyAnF3XdzTFPoa65Hv3kE5gP\n/B+MT/xlirofRq8OAlDV16BnFGM2bEetvA5V92ejFoDRfh/6920UvOc6/DPH6GU0JeLpIkrKOo+o\neVHmVFm0GFU0A/2bxzHfPDG0sAnBxUxQQ9uUYaVnrK+h1xcUomquTf+a05F6uqDsssk7X0qlv/eM\ncf0t6MXvwtz9Vetzsjb4WS6ajJJA+k+RDHsGgXHqVdTMYozNX8P85jbMh75kLVH3px8HNLq9FXXV\nalQc/f/V4isx7tqO+cPvYjZsh6vXYHx0A6p0nLn44012tHMWzsD4agO65Wfop1vQv3sK3lmJUfch\nePdKqw78hWeh7wJF77uF3uRTklrxVgel+0kxJ4eC995If+vP0cePJj4Nxxj0wV9hfP4rqLFGeafD\nic7sDAKT+HCsFl6OcdcOzF33opu+b22UkoDNjF+rgppTivHlXeh/ewz9i39HdzxtDTvvv2hVGcRJ\nVdZgXPlu9K+a0L/4MeYLz6Ju+Sjq/R9G5SUzQnX8kgCAKrsM9fHPoj/0SfTTv7aeahsfgHkLUHUf\nQh/+HZQ4yK96D7jPJ5GGNJmE6qB4zP78l/B+7LMRpw0ubE7kgufj/TOta9Em+sgL6Mcewdx5D8YX\ndkQvgaSQPtE5fntVxtKT+qSsLq/A+KsvWw9okNqpU6KeVAaLZY4YdesAatZsVP0X0KtvwPzB36Gf\n/S1cVg5L3pXQqVRevjWZ2eobMH/09+jmH6Dbf4Px8b+0xiYkk+44nojVjJmo938YfeOH0Ifag5Op\n7bN+d/OHUTkZ9KePpySQpsFEsahwlRAJt6CpldehHWWYjfdjPvh/Me78StonDtO/+HcCHU9baTZy\nrJHkRmh9XsOqrjIMKCzC+NhGVPnb05qeuE1Cm8BIalkVxl3b0cdeGbeNL3UnDH7NrBhg0yAQEs/N\n9Mp3Y2x/GP3UE6jF70p6UjhVOo+c/70N/dJzmP/yXcw9X4eqVRj1X0DFWxSN0qtp3PPm5Fg3o5pr\n4dgr6Gd/i6r7s4TTn1bx5mmG1qmORy15F8YXv4W55+uYu7+C8YWvp2ckOaCuvckaTKi1NYWINq3u\nzqGfTTP4fQBOHMPc+w2Me3eP+/7TgwPQZ1UcBrQf7XKnLsEFBVM+gZ+qrEFVTta09lIdlEFCT9Tx\nvVrlF6De/+GUnFktr8bYsQf9Hz9D//QHmLu+grH5a6PXDB5TMN1GEnPoBOdRUQmWZCZNXB+MLIwC\nYK3bvO2bmA99GfNv/8a68c4vT/2Jlq7AWB19YZ5IuvNlzG/fi/nYIxh/9aWoDzfm1++yBiECKV8y\nRRkY9/8dat7bSMssoplGqoMySBJP1KmkcvNQf/Ln6D96O+a+nZjf2oax5T6UM8a8NXFUY2WtDGkT\nSBc122HVQW//PPqV59MTBBJJz5JlqD+/w5pA79fNqJujPOR43LC8GlVzLbOKZ9Hbl6LuBG+eQLf8\nzJoVd97bbBEDMpU9g0CGRGJVWYOx+W+sXgqtj6M+Uj/+DjqxEkzWiKt30OTXGadceMbWDHn/1X0I\n/fJz6F/+G0QLAlqj/uhyjGtvoqisjIspWkJRv3jICgKh9/QUtflMqvBgsalNxkj2HCyWQANruqmK\nZVbPBO9g7BdP25LA5I4TmDIq+HHLkFKNUgq1YBGMt/yiNpOqfozJyMz68bTK0OogmwaB0DcZcleJ\naz59iKeLaFaK53oy63OTnNB1mqm+mAkcT6nxx0NoPRS8UmlUQJwGJb2YMjPw2TMIJNgwnHZKxXdj\nmMYFgfhmEc3yCw/f+CY+CG3s4yezjzH+ey9dXTdHzqMzDf68cZMgkAEyqDoISGCR+GkcBeKqDsry\n687ECcRilULTFgRGBkQbRIEMvTx7B4FM+avEWx2UacErVeK6ngy6cSYrHATSVBJIhor+ABIeLZ2W\nIBA+SeqPnbEy8CEAuwaBkEy5lyojziAQ3iGdqZkSOuaIYbI/+GVi7xBDRU9P+KFjEtoEpmDE8KST\nhuEMkq0lgUxry0ilzPpcpEe62wSSMV7DcDq7JI/VJjAt39gRMjMG2DQIZFovm1g9NEKmdXWQDcYJ\nZGK3yGBgGnMdgrSWBEZWjWVQnqRNBv79sWsQyLCCgNUwnEBJIGMSniLxlIQy7IOTlHAX0QwrCcDY\nDyHpfOgwxhgzMc3e1qNIdVAGycTqIDt3EYU4PxfZfuEZ+CQ43tiFUGBIR0lgZF7Yok0g+DWD/vww\nBdNGHD58mMceewzTNLnxxhtZt27dZCdhSKa86cbpoTFMpgWvVIl3Kulsv+xMrA4a64k8JJ1tAmPm\nRbb/gWPJzCgwqSUB0zQ5cOAAX/7yl2loaOC3v/0tp06dmswkBGXWHyHh6qBMCV4pM92uJ4oMmzYC\niFEdlMaSQMR5s3Zd5ERl6Od2UoNAZ2cn8+fP57LLLiM3N5c1a9bQ0dExmUmwhB+oM+SPohQ6rpLA\n0OunlXhGDEPWX7ca2SMmE4wXmNL5fgufN3Jb6k+TWTLw788kVwe5XC5KS0vDP5eWlvLqq6+m5VyB\nR++n23WOgN8/+pehydoy5aaiFLz0HIHtnx//dYHA0OunE6XglRfGv/6+XqbFXUIZ6KeeIPDsb1N3\nTP84E8DFTI+Vp+b9W0Y/8YdLAumbNsL84XchtM7vdPj7jicUA37+IwJP/SKuXYyvPpzkUrTxy8ip\npFtaWmhpaQFg586dlJXFmGd/DL2LLscsLiY3SoOrKlrJrKqVGOHpfadO/7q/YPDQf8X1WnXlCorf\newM5SeRJpNzc3KTyNR36/+wTDD7TPv6LLq9gxk0fJD/NaU53vvR9rB7/ieMpP65a9scUr6pN+H3h\nr72Ji2ffQI/1sASoimXMXHsTuWVlKc0bXTKb3vf/GWbvBWvD4qXMvOED5GXIezJR8eSNLi2l70Mf\nJ3DurbiPW1JWlvYgoPQkVsgdPXqUH//4x9x7770ANDU1AfDhD4+/atfp06eTOl9ZWRndKZr/fLqR\nvBmb5Et0kjfRZWLeLFiwIK7XTWqbwOLFizlz5gxdXV34/X7a29upqZms9T2FEEKMNKnVQTk5OXzm\nM5/hgQcewDRNbrjhBhYuXDiZSRBCCBFh0tsErrrqKq666qrJPq0QQogx2HPEsBBCCECCgBBC2JoE\nASGEsDEJAkIIYWMSBIQQwsYmdbCYEEKIzDKtSwLbtm2b6iRkLMmbsUm+RCd5E1025820DgJCCCHG\nJ0FACCFsbFoHgbq6uqlOQsaSvBmb5Et0kjfRZXPeSMOwEELY2LQuCQghhBhfRi4qM1EZtZh9Gu3d\nu5dDhw5RUlLCrl27AOjr66OhoYFz584xd+5ctmzZQnGxtXBOU1MTra2tGIZBfX09VVVVABw/fpzG\nxka8Xi/V1dXU19ejlMLn8/Hoo49y/PhxZs2axebNm5k3b96UXW+8uru7aWxs5Pz58yilqKur45Zb\nbpG8AbxeLzt27MDv9xMIBFi1ahW33Xab5E2QaZps27YNp9PJtm3b7JEvepoJBAL6zjvv1GfPntU+\nn0/ffffd+o033pjqZKXFSy+9pI8dO6a3bt0a3vb9739fNzU1aa21bmpq0t///ve11lq/8cYb+u67\n79Zer1e/9dZb+s4779SBQEBrrfW2bdv0kSNHtGma+oEHHtCHDh3SWmv9y1/+Un/nO9/RWmv99NNP\n6927d0/m5SXN5XLpY8eOaa21vnTpkr7rrrv0G2+8IXmjtTZNU/f392uttfb5fPpLX/qSPnLkiORN\n0OOPP64ffvhh/eCDD2qt7fF5mnbVQRmzmP0kWLZsWfipJKSjo4Pa2loAamtrw9fe0dHBmjVryMvL\nY968ecyfP5/Ozk7cbjf9/f0sXboUpRRr164N7/PMM89w/fXXA7Bq1SpefPFFdBY0ITkcDq644goA\nioqKKC8vx+VySd5gLXZfWFgIQCAQIBAIoJSSvAF6eno4dOgQN954Y3ibHfJl2gWBsRazd7lcU5ii\nyeXxeHA4HADMmTMHj8cDjM4Xp9OJy+UaN78if5eTk8OMGTPo7e2drEtJia6uLl577TWWLFkieRNk\nmib33HMPGzdupLKykoqKCskb4Hvf+x633347Sg0teG+HfJl2QUAMUUoNe0PbzcDAALt27eKOO+5g\nxowZw35n57wxDIOHHnqIffv2cezYMU6ePDns93bMm2effZaSkpJwCXIs0zVfpl3DsNPppKenJ/xz\nT08PTqdzClM0uUpKSnC73TgcDtxuN7NnzwZG54vL5cLpdI6bX6HflZaWEggEuHTpErNmzZrcC0qS\n3+9n165dXHfddVxzzTWA5M1IM2fOZPny5Rw+fNj2eXPkyBGeeeYZnnvuObxeL/39/ezZs8cW+TLt\nSgJ2X8y+pqaGtrY2ANra2li5cmV4e3t7Oz6fj66uLs6cOcOSJUtwOBwUFRVx9OhRtNYcPHgwnF9X\nX301Tz31FAC/+93vWL58eVY8CWmt2bdvH+Xl5dx6663h7ZI3cOHCBS5evAhYPYWef/55ysvLbZ83\nn/zkJ9m3bx+NjY1s3ryZFStWcNddd9kiX6blYLFDhw7xD//wD+HF7NevXz/VSUqLhx9+mJdffpne\n3l5KSkq47bbbWLlyJQ0NDXR3d4/q0vaTn/yEJ598EsMwuOOOO6iurgbg2LFj7N27F6/XS1VVFZ/5\nzGdQSuH1enn00Ud57bXXKC4uZvPmzVx22WVTeclxeeWVV9i+fTuLFi0Kf8g+8YlPUFFRYfu8OXHi\nBI2NjZimidaa1atX85GPfITe3l7b503ISy+9xOOPP862bdtskS/TMggIIYSIz7SrDhJCCBE/CQJC\nCGFjEgSEEMLGJAgIIYSNSRAQQggbkyAghBA2JkFACCFsTIKAEELY2P8HXDqUSSMnqIQAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcedb7e79d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(interp_pitch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0680272,  0.0680499,  0.0680726,  0.0680952,  0.0681179,\n",
       "        0.0681406,  0.0681633,  0.0681859,  0.0682086,  0.0682313,\n",
       "        0.068254 ,  0.0682766,  0.0682993,  0.068322 ,  0.0683447,\n",
       "        0.0683673,  0.06839  ,  0.0684127,  0.0684354,  0.068458 ,\n",
       "        0.0684807,  0.0685034,  0.0685261,  0.0685488,  0.0685714,\n",
       "        0.0685941,  0.0686168,  0.0686395,  0.0686621,  0.0686848,\n",
       "        0.0687075,  0.0687302,  0.0687528,  0.0687755,  0.0687982,\n",
       "        0.0688209,  0.0688435,  0.0688662,  0.0688889,  0.0689116,\n",
       "        0.0689342,  0.0689569,  0.0689796,  0.0690023,  0.0690249,\n",
       "        0.0690476,  0.0690703,  0.069093 ,  0.0691156,  0.0691383,\n",
       "        0.069161 ,  0.0691837,  0.0692063,  0.069229 ,  0.0692517,\n",
       "        0.0692744,  0.0692971,  0.0693197,  0.0693424,  0.0693651,\n",
       "        0.0693878,  0.0694104,  0.0694331,  0.0694558,  0.0694785,\n",
       "        0.0695011,  0.0695238,  0.0695465,  0.0695692,  0.0695918,\n",
       "        0.0696145,  0.0696372,  0.0696599,  0.0696825,  0.0697052,\n",
       "        0.0697279,  0.0697506,  0.0697732,  0.0697959,  0.0698186,\n",
       "        0.0698413,  0.0698639,  0.0698866,  0.0699093,  0.069932 ,\n",
       "        0.0699546,  0.0699773,  0.07     ,  0.0700227,  0.0700454,\n",
       "        0.070068 ,  0.0700907,  0.0701134,  0.0701361,  0.0701587,\n",
       "        0.0701814,  0.0702041,  0.0702268,  0.0702494,  0.0702721,\n",
       "        0.0702948,  0.0703175,  0.0703401,  0.0703628,  0.0703855,\n",
       "        0.0704082,  0.0704308,  0.0704535,  0.0704762,  0.0704989,\n",
       "        0.0705215,  0.0705442,  0.0705669,  0.0705896,  0.0706122,\n",
       "        0.0706349,  0.0706576,  0.0706803,  0.0707029,  0.0707256,\n",
       "        0.0707483,  0.070771 ,  0.0707937,  0.0708163,  0.070839 ,\n",
       "        0.0708617,  0.0708844,  0.070907 ,  0.0709297,  0.0709524,\n",
       "        0.0709751,  0.0709977,  0.0710204,  0.0710431,  0.0710658,\n",
       "        0.0710884,  0.0711111,  0.0711338,  0.0711565,  0.0711791,\n",
       "        0.0712018,  0.0712245,  0.0712472,  0.0712698,  0.0712925,\n",
       "        0.0713152,  0.0713379,  0.0713605,  0.0713832,  0.0714059,\n",
       "        0.0714286,  0.0714512,  0.0714739,  0.0714966,  0.0715193,\n",
       "        0.071542 ,  0.0715646,  0.0715873,  0.07161  ,  0.0716327,\n",
       "        0.0716553,  0.071678 ,  0.0717007,  0.0717234,  0.071746 ,\n",
       "        0.0717687,  0.0717914,  0.0718141,  0.0718367,  0.0718594,\n",
       "        0.0718821,  0.0719048,  0.0719274,  0.0719501,  0.0719728,\n",
       "        0.0719955,  0.0720181,  0.0720408,  0.0720635,  0.0720862,\n",
       "        0.0721088,  0.0721315,  0.0721542,  0.0721769,  0.0721995,\n",
       "        0.0722222,  0.0722449,  0.0722676,  0.0722902,  0.0723129,\n",
       "        0.0723356,  0.0723583,  0.072381 ,  0.0724036,  0.0724263,\n",
       "        0.072449 ,  0.0724717,  0.0724943,  0.072517 ,  0.0725397,\n",
       "        0.0725624,  0.072585 ,  0.0726077,  0.0726304,  0.0726531,\n",
       "        0.0726757,  0.0726984,  0.0727211,  0.0727438,  0.0727664,\n",
       "        0.0727891,  0.0728118,  0.0728345,  0.0728571,  0.0728798,\n",
       "        0.0729025,  0.0729252,  0.0729478,  0.0729705,  0.0729932,\n",
       "        0.0730159,  0.0730385,  0.0730612,  0.0730839,  0.0731066,\n",
       "        0.0731293,  0.0731519,  0.0731746,  0.0731973,  0.07322  ,\n",
       "        0.0732426,  0.0732653,  0.073288 ,  0.0733107,  0.0733333,\n",
       "        0.073356 ,  0.0733787,  0.0734014,  0.073424 ,  0.0734467,\n",
       "        0.0734694,  0.0734921,  0.0735147,  0.0735374,  0.0735601,\n",
       "        0.0735828,  0.0736054,  0.0736281,  0.0736508,  0.0736735,\n",
       "        0.0736961,  0.0737188,  0.0737415,  0.0737642,  0.0737868,\n",
       "        0.0738095,  0.0738322,  0.0738549,  0.0738776,  0.0739002,\n",
       "        0.0739229,  0.0739456,  0.0739683,  0.0739909,  0.0740136,\n",
       "        0.0740363,  0.074059 ,  0.0740816,  0.0741043,  0.074127 ,\n",
       "        0.0741497,  0.0741723,  0.074195 ,  0.0742177,  0.0742404,\n",
       "        0.074263 ,  0.0742857,  0.0743084,  0.0743311,  0.0743537,\n",
       "        0.0743764,  0.0743991,  0.0744218,  0.0744444,  0.0744671,\n",
       "        0.0744898,  0.0745125,  0.0745351,  0.0745578,  0.0745805,\n",
       "        0.0746032,  0.0746259,  0.0746485,  0.0746712,  0.0746939,\n",
       "        0.0747166,  0.0747392,  0.0747619,  0.0747846,  0.0748073,\n",
       "        0.0748299,  0.0748526,  0.0748753,  0.074898 ,  0.0749206,\n",
       "        0.0749433,  0.074966 ,  0.0749887,  0.0750113,  0.075034 ,\n",
       "        0.0750567,  0.0750794,  0.075102 ,  0.0751247,  0.0751474,\n",
       "        0.0751701,  0.0751927,  0.0752154,  0.0752381,  0.0752608,\n",
       "        0.0752834,  0.0753061,  0.0753288,  0.0753515,  0.0753741,\n",
       "        0.0753968,  0.0754195,  0.0754422,  0.0754649,  0.0754875,\n",
       "        0.0755102,  0.0755329,  0.0755556,  0.0755782,  0.0756009,\n",
       "        0.0756236,  0.0756463,  0.0756689,  0.0756916,  0.0757143,\n",
       "        0.075737 ,  0.0757596,  0.0757823,  0.075805 ,  0.0758277,\n",
       "        0.0758503,  0.075873 ,  0.0758957,  0.0759184,  0.075941 ,\n",
       "        0.0759637,  0.0759864,  0.0760091,  0.0760317,  0.0760544,\n",
       "        0.0760771,  0.0760998,  0.0761224,  0.0761451,  0.0761678,\n",
       "        0.0761905,  0.0762132,  0.0762358,  0.0762585,  0.0762812,\n",
       "        0.0763039,  0.0763265,  0.0763492,  0.0763719,  0.0763946,\n",
       "        0.0764172,  0.0764399,  0.0764626,  0.0764853,  0.0765079,\n",
       "        0.0765306,  0.0765533,  0.076576 ,  0.0765986,  0.0766213,\n",
       "        0.076644 ,  0.0766667,  0.0766893,  0.076712 ,  0.0767347,\n",
       "        0.0767574,  0.07678  ,  0.0768027,  0.0768254,  0.0768481,\n",
       "        0.0768707,  0.0768934,  0.0769161,  0.0769388,  0.0769615,\n",
       "        0.0769841,  0.0770068,  0.0770295,  0.0770522,  0.0770748,\n",
       "        0.0770975,  0.0771202,  0.0771429,  0.0771655,  0.0771882,\n",
       "        0.0772109,  0.0772336,  0.0772562,  0.0772789,  0.0773016,\n",
       "        0.0773243,  0.0773469,  0.0773696,  0.0773923,  0.077415 ,\n",
       "        0.0774376,  0.0774603,  0.077483 ,  0.0775057,  0.0775283,\n",
       "        0.077551 ,  0.0775737,  0.0775964,  0.077619 ,  0.0776417,\n",
       "        0.0776644,  0.0776871,  0.0777098,  0.0777324,  0.0777551,\n",
       "        0.0777778,  0.0778005,  0.0778231,  0.0778458,  0.0778685,\n",
       "        0.0778912,  0.0779138,  0.0779365,  0.0779592,  0.0779819,\n",
       "        0.0780045,  0.0780272,  0.0780499,  0.0780726,  0.0780952,\n",
       "        0.0781179,  0.0781406,  0.0781633,  0.0781859,  0.0782086,\n",
       "        0.0782313,  0.078254 ,  0.0782766,  0.0782993,  0.078322 ,\n",
       "        0.0783447,  0.0783673,  0.07839  ,  0.0784127,  0.0784354,\n",
       "        0.078458 ,  0.0784807,  0.0785034,  0.0785261,  0.0785488,\n",
       "        0.0785714,  0.0785941,  0.0786168,  0.0786395,  0.0786621,\n",
       "        0.0786848,  0.0787075,  0.0787302,  0.0787528,  0.0787755,\n",
       "        0.0787982,  0.0788209,  0.0788435,  0.0788662,  0.0788889,\n",
       "        0.0789116,  0.0789342,  0.0789569,  0.0789796,  0.0790023,\n",
       "        0.0790249,  0.0790476,  0.0790703,  0.079093 ,  0.0791156,\n",
       "        0.0791383,  0.079161 ,  0.0791837,  0.0792063,  0.079229 ,\n",
       "        0.0792517,  0.0792744,  0.0792971,  0.0793197,  0.0793424,\n",
       "        0.0793651,  0.0793878,  0.0794104,  0.0794331,  0.0794558,\n",
       "        0.0794785,  0.0795011,  0.0795238,  0.0795465,  0.0795692,\n",
       "        0.0795918,  0.0796145,  0.0796372,  0.0796599,  0.0796825,\n",
       "        0.0797052,  0.0797279,  0.0797506,  0.0797732,  0.0797959,\n",
       "        0.0798186,  0.0798413,  0.0798639,  0.0798866,  0.0799093,\n",
       "        0.079932 ,  0.0799546,  0.0799773,  0.08     ,  0.0800227,\n",
       "        0.0800454,  0.080068 ,  0.0800907,  0.0801134,  0.0801361,\n",
       "        0.0801587,  0.0801814,  0.0802041,  0.0802268,  0.0802494,\n",
       "        0.0802721,  0.0802948,  0.0803175,  0.0803401,  0.0803628,\n",
       "        0.0803855,  0.0804082,  0.0804308,  0.0804535,  0.0804762,\n",
       "        0.0804989,  0.0805215,  0.0805442,  0.0805669,  0.0805896,\n",
       "        0.0806122,  0.0806349,  0.0806576,  0.0806803,  0.0807029,\n",
       "        0.0807256,  0.0807483,  0.080771 ,  0.0807937,  0.0808163,\n",
       "        0.080839 ,  0.0808617,  0.0808844,  0.080907 ,  0.0809297,\n",
       "        0.0809524,  0.0809751,  0.0809977,  0.0810204,  0.0810431,\n",
       "        0.0810658,  0.0810884,  0.0811111,  0.0811338,  0.0811565,\n",
       "        0.0811791,  0.0812018,  0.0812245,  0.0812472,  0.0812698,\n",
       "        0.0812925,  0.0813152,  0.0813379,  0.0813605,  0.0813832,\n",
       "        0.0814059,  0.0814286,  0.0814512,  0.0814739,  0.0814966,\n",
       "        0.0815193,  0.081542 ,  0.0815646,  0.0815873,  0.08161  ,\n",
       "        0.0816327,  0.0816553,  0.081678 ,  0.0817007,  0.0817234,\n",
       "        0.081746 ,  0.0817687,  0.0817914,  0.0818141,  0.0818367,\n",
       "        0.0818594,  0.0818821,  0.0819048,  0.0819274,  0.0819501,\n",
       "        0.0819728,  0.0819955,  0.0820181,  0.0820408,  0.0820635,\n",
       "        0.0820862,  0.0821088,  0.0821315,  0.0821542,  0.0821769,\n",
       "        0.0821995,  0.0822222,  0.0822449,  0.0822676,  0.0822902,\n",
       "        0.0823129,  0.0823356,  0.0823583,  0.082381 ,  0.0824036,\n",
       "        0.0824263,  0.082449 ,  0.0824717,  0.0824943,  0.082517 ,\n",
       "        0.0825397,  0.0825624,  0.082585 ,  0.0826077,  0.0826304,\n",
       "        0.0826531,  0.0826757,  0.0826984,  0.0827211,  0.0827438,\n",
       "        0.0827664,  0.0827891,  0.0828118,  0.0828345,  0.0828571,\n",
       "        0.0828798,  0.0829025,  0.0829252,  0.0829478,  0.0829705,\n",
       "        0.0829932,  0.0830159,  0.0830385,  0.0830612,  0.0830839,\n",
       "        0.0831066,  0.0831293,  0.0831519,  0.0831746,  0.0831973,\n",
       "        0.08322  ,  0.0832426,  0.0832653,  0.083288 ,  0.0833107,\n",
       "        0.0833333,  0.083356 ,  0.0833787,  0.0834014,  0.083424 ,\n",
       "        0.0834467,  0.0834694,  0.0834921,  0.0835147,  0.0835374,\n",
       "        0.0835601,  0.0835828,  0.0836054,  0.0836281,  0.0836508,\n",
       "        0.0836735,  0.0836961,  0.0837188,  0.0837415,  0.0837642,\n",
       "        0.0837868,  0.0838095,  0.0838322,  0.0838549,  0.0838776,\n",
       "        0.0839002,  0.0839229,  0.0839456,  0.0839683,  0.0839909,\n",
       "        0.0840136,  0.0840363,  0.084059 ,  0.0840816,  0.0841043,\n",
       "        0.084127 ,  0.0841497,  0.0841723,  0.084195 ,  0.0842177,\n",
       "        0.0842404,  0.084263 ,  0.0842857,  0.0843084,  0.0843311,\n",
       "        0.0843537,  0.0843764,  0.0843991,  0.0844218,  0.0844444,\n",
       "        0.0844671,  0.0844898,  0.0845125,  0.0845351,  0.0845578,\n",
       "        0.0845805,  0.0846032,  0.0846259,  0.0846485,  0.0846712,\n",
       "        0.0846939,  0.0847166,  0.0847392,  0.0847619,  0.0847846,\n",
       "        0.0848073,  0.0848299,  0.0848526,  0.0848753,  0.084898 ,\n",
       "        0.0849206,  0.0849433,  0.084966 ,  0.0849887,  0.0850113,\n",
       "        0.085034 ,  0.0850567,  0.0850794,  0.085102 ,  0.0851247,\n",
       "        0.0851474,  0.0851701,  0.0851927,  0.0852154,  0.0852381,\n",
       "        0.0852608,  0.0852834,  0.0853061,  0.0853288,  0.0853515,\n",
       "        0.0853741,  0.0853968,  0.0854195,  0.0854422,  0.0854649,\n",
       "        0.0854875,  0.0855102,  0.0855329,  0.0855556,  0.0855782,\n",
       "        0.0856009,  0.0856236,  0.0856463,  0.0856689,  0.0856916,\n",
       "        0.0857143,  0.085737 ,  0.0857596,  0.0857823,  0.085805 ,\n",
       "        0.0858277,  0.0858503,  0.085873 ,  0.0858957,  0.0859184,\n",
       "        0.085941 ,  0.0859637,  0.0859864,  0.0860091,  0.0860317,\n",
       "        0.0860544,  0.0860771,  0.0860998,  0.0861224,  0.0861451,\n",
       "        0.0861678,  0.0861905,  0.0862132,  0.0862358,  0.0862585,\n",
       "        0.0862812,  0.0863039,  0.0863265,  0.0863492,  0.0863719,\n",
       "        0.0863946,  0.0864172,  0.0864399,  0.0864626,  0.0864853,\n",
       "        0.0865079,  0.0865306,  0.0865533,  0.086576 ,  0.0865986,\n",
       "        0.0866213,  0.086644 ,  0.0866667,  0.0866893,  0.086712 ,\n",
       "        0.0867347,  0.0867574,  0.08678  ,  0.0868027,  0.0868254,\n",
       "        0.0868481,  0.0868707,  0.0868934,  0.0869161,  0.0869388,\n",
       "        0.0869615,  0.0869841,  0.0870068,  0.0870295,  0.0870522,\n",
       "        0.0870748,  0.0870975,  0.0871202,  0.0871429,  0.0871655,\n",
       "        0.0871882,  0.0872109,  0.0872336,  0.0872562,  0.0872789,\n",
       "        0.0873016,  0.0873243,  0.0873469,  0.0873696,  0.0873923,\n",
       "        0.087415 ,  0.0874376,  0.0874603,  0.087483 ,  0.0875057,\n",
       "        0.0875283,  0.087551 ,  0.0875737,  0.0875964,  0.087619 ,\n",
       "        0.0876417,  0.0876644,  0.0876871,  0.0877098,  0.0877324,\n",
       "        0.0877551,  0.0877778,  0.0878005,  0.0878231,  0.0878458,\n",
       "        0.0878685,  0.0878912,  0.0879138,  0.0879365,  0.0879592,\n",
       "        0.0879819,  0.0880045,  0.0880272,  0.0880499,  0.0880726,\n",
       "        0.0880952,  0.0881179,  0.0881406,  0.0881633,  0.0881859,\n",
       "        0.0882086,  0.0882313,  0.088254 ,  0.0882766,  0.0882993,\n",
       "        0.088322 ,  0.0883447,  0.0883673,  0.08839  ,  0.0884127,\n",
       "        0.0884354,  0.088458 ,  0.0884807,  0.0885034,  0.0885261,\n",
       "        0.0885488,  0.0885714,  0.0885941,  0.0886168,  0.0886395,\n",
       "        0.0886621,  0.0886848,  0.0887075,  0.0887302,  0.0887528,\n",
       "        0.0887755,  0.0887982,  0.0888209,  0.0888435,  0.0888662,\n",
       "        0.0888889,  0.0889116,  0.0889342,  0.0889569,  0.0889796,\n",
       "        0.0890023,  0.0890249,  0.0890476,  0.0890703,  0.089093 ,\n",
       "        0.0891156,  0.0891383,  0.089161 ,  0.0891837,  0.0892063,\n",
       "        0.089229 ,  0.0892517,  0.0892744,  0.0892971,  0.0893197,\n",
       "        0.0893424,  0.0893651,  0.0893878,  0.0894104,  0.0894331,\n",
       "        0.0894558,  0.0894785,  0.0895011,  0.0895238,  0.0895465,\n",
       "        0.0895692,  0.0895918,  0.0896145,  0.0896372,  0.0896599,\n",
       "        0.0896825,  0.0897052,  0.0897279,  0.0897506,  0.0897732,\n",
       "        0.0897959,  0.0898186,  0.0898413,  0.0898639,  0.0898866,\n",
       "        0.0899093,  0.089932 ,  0.0899546,  0.0899773,  0.09     ,\n",
       "        0.0900227,  0.0900454,  0.090068 ,  0.0900907,  0.0901134,\n",
       "        0.0901361,  0.0901587,  0.0901814,  0.0902041,  0.0902268,\n",
       "        0.0902494,  0.0902721,  0.0902948,  0.0903175,  0.0903401,\n",
       "        0.0903628,  0.0903855,  0.0904082,  0.0904308,  0.0904535,\n",
       "        0.0904762,  0.0904989,  0.0905215,  0.0905442,  0.0905669,\n",
       "        0.0905896,  0.0906122,  0.0906349,  0.0906576,  0.0906803])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env_list[3000:4000,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out_pitch = np.array([np.arange(t.size), t, interp_pitch]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "name": "tf12"
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   "codemirror_mode": {
    "name": "ipython",
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