{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "from audio.bitstream import BitStream\n",
    "from audio.coders import rice\n",
    "import audio.wave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preamble: Rice Coder\n",
    "============"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0000000000101010"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(42, uint16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "frame = [0,0,1,1,7,255,301,16,78, 100, 0,0,0, 6]\n",
    "r = rice.from_frame(frame, signed=False)\n",
    "print r.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(0,r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000010"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(1,r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1111111111110"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(255, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000000000000000010000010001110111111111111001101111111111010000001110110001001110000000000000000000001100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(frame, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0000010"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BitStream(1,rice(b=5,signed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 779\n",
      "1 408\n",
      "2 230\n",
      "3 147\n",
      "4 114\n",
      "5 105\n",
      "6 107\n",
      "7 115\n",
      "8 127\n"
     ]
    }
   ],
   "source": [
    "for b in range(0,9):\n",
    "    print b, len(BitStream(frame, rice(signed=False, b=b)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rice Amplitude Coder (Audio)\n",
    "================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FGV67aMnni+YuBAJyaHT2vM7CDFm555Z4smAftHXV8xB6Bjx3DvLs2O7brtvl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33868a8750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = 44100.0; dt = 1.0 / df\n",
    "f = 440.0\n",
    "t = r_[0:0.1:dt] # 0.1 sec of data\n",
    "data = cos(2*pi*f*t)\n",
    "#data = audio.io.record(1.0, df=df)[0]\n",
    "plot(data)\n",
    "data2 = wave.read(wave.write(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16\n",
      "4410\n"
     ]
    }
   ],
   "source": [
    "data3 = wave.read(wave.write(data), scale=False)\n",
    "print data3.dtype\n",
    "data = data3[0]\n",
    "print len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "r = rice.from_frame(data, signed=True) # near 15 ?\n",
    "print r.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70560\n",
      "73510\n"
     ]
    }
   ],
   "source": [
    "stream = BitStream(data, r)\n",
    "print len(data) * 16\n",
    "print len(stream) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-32768\n"
     ]
    },
    {
     "data": {
      "image/png": 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WD0YXnL8IvBR4LvBZYOcaqOtLwFWL+n4V2N/a+4F3tvbOVvcGYHt7P+vashPAdUCAjwLf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\nQJI69v/kYJR8BkhEaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338cf8b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close()\n",
    "hist(abs(data.astype(int32)))\n",
    "# negative values of abs(data) ? WTF!!!\n",
    "print abs(int16(-2**15)) # overflow: abs output is a int16!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59956\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  282.,   284.,   288.,   300.,   316.,   336.,   372.,   424.,\n",
       "          544.,  1264.]),\n",
       " array([    0. ,   327.7,   655.4,   983.1,  1310.8,  1638.5,  1966.2,\n",
       "         2293.9,  2621.6,  2949.3,  3277. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BngN8Fdg07b5O0ue3gYtPqP05sKONdwAfaONN7XWsBDa017diifp8DfAK4J4hfQK3A5cD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\nHfs/lv/8SmTEQ14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338e103e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = 44100.0; dt = 1.0 / df\n",
    "f = 440.0\n",
    "t = r_[0:0.1:dt] # 0.1 sec of data\n",
    "data = 0.1*sin(2*pi*f*t)\n",
    "data = wave.read(wave.write(data), scale=False)[0]\n",
    "print len(BitStream(data, rice.from_frame(data, signed=True)))\n",
    "hist(abs(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Differential Rice Coder\n",
    "============="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = 44100.0; dt = 1.0 / df\n",
    "f = 440.0\n",
    "t = r_[0:0.1:dt] # 0.1 sec of data\n",
    "#data = sin(2*pi*f*t)\n",
    "#data = wave.read(wave.write(data), scale=False)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4410 4409\n"
     ]
    }
   ],
   "source": [
    "ddata = diff(data)\n",
    "print len(data), len(ddata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4410 4410\n"
     ]
    }
   ],
   "source": [
    "ddata = diff(r_[0,data]) # coding of the first value\n",
    "print len(data), len(ddata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  281.,   288.,   288.,   300.,   312.,   340.,   384.,   412.,\n",
       "          560.,  1245.]),\n",
       " array([   0. ,   20.6,   41.2,   61.8,   82.4,  103. ,  123.6,  144.2,\n",
       "         164.8,  185.4,  206. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rdkxd2+779T5PZ4APVtUG4Hpge5uPJXdMXXIhgF9NMY7NwJ7W3gPcPMGxTERVfQ34wTnl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\nGI3K7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d042610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(abs(ddata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "42363\n"
     ]
    }
   ],
   "source": [
    "r = rice.from_frame(ddata, signed=True)\n",
    "print r.b\n",
    "print len(BitStream(ddata, r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata = cumsum(ddata)\n",
    "all(data == idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "24747\n"
     ]
    }
   ],
   "source": [
    "dddata = diff(r_[0, diff(r_[0, data])])\n",
    "r = rice.from_frame(dddata, signed=True)\n",
    "print r.b\n",
    "print len(BitStream(dddata, r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iidata = cumsum(cumsum(dddata))\n",
    "all(data == iidata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}