wav file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wave\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pyaudio\n",
    "import time\n",
    "import bitstring\n",
    "\n",
    "from common import make_sine_wave, make_silence, bytes_to_arrays, normalized_wf_to_bytes, string_to_binary\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fn = \"yoga-1.wav\"\n",
    "\n",
    "wf = wave.open(fn , 'rb')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__del__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__enter__',\n",
       " '__eq__',\n",
       " '__exit__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_compname',\n",
       " '_comptype',\n",
       " '_convert',\n",
       " '_data_chunk',\n",
       " '_data_seek_needed',\n",
       " '_file',\n",
       " '_fmt_chunk_read',\n",
       " '_framerate',\n",
       " '_framesize',\n",
       " '_i_opened_the_file',\n",
       " '_nchannels',\n",
       " '_nframes',\n",
       " '_read_fmt_chunk',\n",
       " '_sampwidth',\n",
       " '_soundpos',\n",
       " 'close',\n",
       " 'getcompname',\n",
       " 'getcomptype',\n",
       " 'getfp',\n",
       " 'getframerate',\n",
       " 'getmark',\n",
       " 'getmarkers',\n",
       " 'getnchannels',\n",
       " 'getnframes',\n",
       " 'getparams',\n",
       " 'getsampwidth',\n",
       " 'initfp',\n",
       " 'readframes',\n",
       " 'rewind',\n",
       " 'setpos',\n",
       " 'tell']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getframerate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getnchannels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getsampwidth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getsampwidth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345796"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getnframes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<chunk.Chunk at 0x7f6de8624220>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.getfp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wf.readframes(wf.getnframes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytes"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method readframes in module wave:\n",
      "\n",
      "readframes(nframes) method of wave.Wave_read instance\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# I want to convert this to two series\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data) / wf.getnframes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def bytes_to_arrays(data,samplewidth=None, channels=None):\n",
    "#    \n",
    "#    factor = 2 ** (8*samplewidth-1) - 1\n",
    "    \n",
    "#    if channels == 1:\n",
    "#        left = np.array( list( map( lambda d : int.from_bytes( d , \"little\", signed=True ) , [data[i+0:i+samplewidth+0] for i in range(0, len(data), samplewidth*channels)] ) ) ) / factor\n",
    "        \n",
    "#        return left\n",
    "        \n",
    "#    elif channels == 2:\n",
    "#        left = np.array( list( map( lambda d : int.from_bytes( d , \"little\", signed=True ) , [data[i+0:i+samplewidth+0] for i in range(0, len(data), samplewidth*channels)] ) ) ) / factor\n",
    "#        right = np.array( list( map( lambda d : int.from_bytes( d , \"little\", signed=True ) , [data[i+2:i+samplewidth+2] for i in range(0, len(data), samplewidth*channels)] ) ) ) / factor\n",
    "        \n",
    "#        return left,right\n",
    "        \n",
    "#    else:\n",
    "#        raise Exception(\"channels must be 1 or 2\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "left,right = bytes_to_arrays( data, samplewidth=wf.getsampwidth(), channels=wf.getnchannels() ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2000.0, 2000.0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure( figsize=(15,12) )\n",
    "plt.plot(left)\n",
    "plt.xlim([300000,302000])\n",
    "plt.ylim([-2000,2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate an audio signal:\n",
    "\n",
    "\n",
    "#def normalized_wf_to_bytes( signal, channels=1,width=2,fs=44100):\n",
    "#    \n",
    "#    if channels != 1:\n",
    "#        raise Exception(\"only channels=1 is implemented\")\n",
    "#    \n",
    "#    factor = 2 ** (8*width-1) - 1\n",
    "#    \n",
    "#    return b\"\".join( map( lambda e : int(e*factor).to_bytes(width,\"little\", signed=True), s ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def make_sine_wave(durration, fs, freq ):\n",
    "#    \n",
    "#    return np.sin( 2 * np.pi * np.arange(0,durration,1/fs) * freq )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = make_sine_wave(0.01, 44100, 1000 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "out_data = []\n",
    "\n",
    "fs = 16000\n",
    "\n",
    "#s = make_sine_wave(2, fs, 1000 )\n",
    "s = ba\n",
    "\n",
    "s_bytes = normalized_wf_to_bytes( s, fs=fs )\n",
    "loc = 0\n",
    "start_time = time.time()\n",
    "\n",
    "\n",
    "p = pyaudio.PyAudio()\n",
    "\n",
    "# define callback (2)\n",
    "def callback(in_data, frame_count, time_info, status):\n",
    "    \n",
    "    global loc,s_bytes,out_data,start_time\n",
    "        \n",
    "    data = s_bytes[loc:loc+frame_count*2]\n",
    "    loc = loc + frame_count*2\n",
    "    \n",
    "    out_data.append(in_data)\n",
    "    \n",
    "    if len(data) < ( frame_count*2 ):\n",
    "        data = data + ( int(0).to_bytes(2,\"little\", signed=True) * ( (frame_count*2) - len(data) ) )\n",
    "\n",
    "    if ( time.time() - start_time ) < 5:\n",
    "        ret_val = pyaudio.paContinue\n",
    "    else:\n",
    "        ret_val = pyaudio.paComplete\n",
    "                       \n",
    "    return (data, ret_val)\n",
    "\n",
    "# open stream using callback (3)\n",
    "stream = p.open(format=p.get_format_from_width(2),\n",
    "                channels=1,\n",
    "                rate=fs,\n",
    "                output=True,\n",
    "                stream_callback=callback,\n",
    "                input=True)\n",
    "\n",
    "# start the stream (4)\n",
    "stream.start_stream()\n",
    "\n",
    "# wait for stream to finish (5)\n",
    "while stream.is_active():\n",
    "    time.sleep(0.1)\n",
    "\n",
    "# stop stream (6)\n",
    "stream.stop_stream()\n",
    "stream.close()\n",
    "wf.close()\n",
    "\n",
    "# close PyAudio (7)\n",
    "p.terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "ba = bytes_to_arrays(b\"\".join(out_data),samplewidth=2, channels=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6da4fde5e0>]"
      ]
     },
     "execution_count": 274,
     "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": [
    "plt.plot(ba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.992"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ba)/fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "ba = bytes_to_arrays(s_bytes,samplewidth=2, channels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 50.0)"
      ]
     },
     "execution_count": 232,
     "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": [
    "plt.plot(ba/32767)\n",
    "plt.xlim([0,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 8000\n",
    "\n",
    "signals = []\n",
    "\n",
    "signals.append( make_sine_wave(0.25, fs=fs, freq=500 ) )\n",
    "signals.append( make_silence(0.25, fs=fs ) )\n",
    "signals.append( make_sine_wave(0.25, fs=fs, freq=500 ) )\n",
    "signals.append( make_silence(0.25, fs=fs ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate( signals ).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'Bryce'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Bryce\".encode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BIT_COUNT',\n",
       " 'BRACKET_RE',\n",
       " 'BYTE_REVERSAL_DICT',\n",
       " 'BitArray',\n",
       " 'BitStream',\n",
       " 'BitString',\n",
       " 'Bits',\n",
       " 'ByteAlignError',\n",
       " 'ByteStore',\n",
       " 'CACHE_SIZE',\n",
       " 'ConstBitArray',\n",
       " 'ConstBitStream',\n",
       " 'ConstByteStore',\n",
       " 'CreationError',\n",
       " 'DEFAULT_UINT',\n",
       " 'Error',\n",
       " 'INIT_NAMES',\n",
       " 'InterpretError',\n",
       " 'LEADING_OCT_CHARS',\n",
       " 'LITERAL_RE',\n",
       " 'MAX_CHARS',\n",
       " 'MULTIPLICATIVE_RE',\n",
       " 'MmapByteArray',\n",
       " 'OCT_TO_BITS',\n",
       " 'PACK_CODE_SIZE',\n",
       " 'REPLACEMENTS_BE',\n",
       " 'REPLACEMENTS_LE',\n",
       " 'ReadError',\n",
       " 'STRUCT_PACK_RE',\n",
       " 'STRUCT_SPLIT_RE',\n",
       " 'TOKEN_RE',\n",
       " '__all__',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__licence__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_lsb0',\n",
       " '_switch_lsb0_methods',\n",
       " '_tokenname_to_initialiser',\n",
       " 'array',\n",
       " 'basestring',\n",
       " 'binascii',\n",
       " 'bytealigned',\n",
       " 'byteorder',\n",
       " 'collections',\n",
       " 'collectionsAbc',\n",
       " 'copy',\n",
       " 'equal',\n",
       " 'expand_brackets',\n",
       " 'file',\n",
       " 'i',\n",
       " 'init_with_length_and_offset',\n",
       " 'init_with_length_only',\n",
       " 'init_without_length_or_offset',\n",
       " 'io',\n",
       " 'mmap',\n",
       " 'name_to_read',\n",
       " 'numbers',\n",
       " 'offsetcopy',\n",
       " 'operator',\n",
       " 'os',\n",
       " 'pack',\n",
       " 're',\n",
       " 'set_lsb0',\n",
       " 'set_msb0',\n",
       " 'struct',\n",
       " 'structparser',\n",
       " 'sys',\n",
       " 'tidy_input_string',\n",
       " 'tokenparser',\n",
       " 'xrange']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(bitstring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class BitArray in module bitstring:\n",
      "\n",
      "class BitArray(Bits)\n",
      " |  BitArray(auto=None, length=None, offset=None, **kwargs)\n",
      " |  \n",
      " |  A container holding a mutable sequence of bits.\n",
      " |  \n",
      " |  Subclass of the immutable Bits class. Inherits all of its\n",
      " |  methods (except __hash__) and adds mutating methods.\n",
      " |  \n",
      " |  Mutating methods:\n",
      " |  \n",
      " |  append() -- Append a bitstring.\n",
      " |  byteswap() -- Change byte endianness in-place.\n",
      " |  insert() -- Insert a bitstring.\n",
      " |  invert() -- Flip bit(s) between one and zero.\n",
      " |  overwrite() -- Overwrite a section with a new bitstring.\n",
      " |  prepend() -- Prepend a bitstring.\n",
      " |  replace() -- Replace occurrences of one bitstring with another.\n",
      " |  reverse() -- Reverse bits in-place.\n",
      " |  rol() -- Rotate bits to the left.\n",
      " |  ror() -- Rotate bits to the right.\n",
      " |  set() -- Set bit(s) to 1 or 0.\n",
      " |  \n",
      " |  Methods inherited from Bits:\n",
      " |  \n",
      " |  all() -- Check if all specified bits are set to 1 or 0.\n",
      " |  any() -- Check if any of specified bits are set to 1 or 0.\n",
      " |  count() -- Count the number of bits set to 1 or 0.\n",
      " |  cut() -- Create generator of constant sized chunks.\n",
      " |  endswith() -- Return whether the bitstring ends with a sub-string.\n",
      " |  find() -- Find a sub-bitstring in the current bitstring.\n",
      " |  findall() -- Find all occurrences of a sub-bitstring in the current bitstring.\n",
      " |  join() -- Join bitstrings together using current bitstring.\n",
      " |  rfind() -- Seek backwards to find a sub-bitstring.\n",
      " |  split() -- Create generator of chunks split by a delimiter.\n",
      " |  startswith() -- Return whether the bitstring starts with a sub-bitstring.\n",
      " |  tobytes() -- Return bitstring as bytes, padding if needed.\n",
      " |  tofile() -- Write bitstring to file, padding if needed.\n",
      " |  unpack() -- Interpret bits using format string.\n",
      " |  \n",
      " |  Special methods:\n",
      " |  \n",
      " |  Mutating operators are available: [], <<=, >>=, +=, *=, &=, |= and ^=\n",
      " |  in addition to the inherited [], ==, !=, +, *, ~, <<, >>, &, | and ^.\n",
      " |  \n",
      " |  Properties:\n",
      " |  \n",
      " |  bin -- The bitstring as a binary string.\n",
      " |  bool -- For single bit bitstrings, interpret as True or False.\n",
      " |  bytepos -- The current byte position in the bitstring.\n",
      " |  bytes -- The bitstring as a bytes object.\n",
      " |  float -- Interpret as a floating point number.\n",
      " |  floatbe -- Interpret as a big-endian floating point number.\n",
      " |  floatle -- Interpret as a little-endian floating point number.\n",
      " |  floatne -- Interpret as a native-endian floating point number.\n",
      " |  hex -- The bitstring as a hexadecimal string.\n",
      " |  int -- Interpret as a two's complement signed integer.\n",
      " |  intbe -- Interpret as a big-endian signed integer.\n",
      " |  intle -- Interpret as a little-endian signed integer.\n",
      " |  intne -- Interpret as a native-endian signed integer.\n",
      " |  len -- Length of the bitstring in bits.\n",
      " |  oct -- The bitstring as an octal string.\n",
      " |  pos -- The current bit position in the bitstring.\n",
      " |  se -- Interpret as a signed exponential-Golomb code.\n",
      " |  ue -- Interpret as an unsigned exponential-Golomb code.\n",
      " |  sie -- Interpret as a signed interleaved exponential-Golomb code.\n",
      " |  uie -- Interpret as an unsigned interleaved exponential-Golomb code.\n",
      " |  uint -- Interpret as a two's complement unsigned integer.\n",
      " |  uintbe -- Interpret as a big-endian unsigned integer.\n",
      " |  uintle -- Interpret as a little-endian unsigned integer.\n",
      " |  uintne -- Interpret as a native-endian unsigned integer.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      BitArray\n",
      " |      Bits\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __copy__(self)\n",
      " |      Return a new copy of the BitArray.\n",
      " |  \n",
      " |  __delitem__(self, key)\n",
      " |      Delete item or range.\n",
      " |      \n",
      " |      Indices are in units of the step parameter (default 1 bit).\n",
      " |      Stepping is used to specify the number of bits in each item.\n",
      " |      \n",
      " |      >>> a = BitArray('0x001122')\n",
      " |      >>> del a[1:2:8]\n",
      " |      >>> print a\n",
      " |      0x0022\n",
      " |  \n",
      " |  __iadd__(self, bs)\n",
      " |      Append bs to current bitstring. Return self.\n",
      " |      \n",
      " |      bs -- the bitstring to append.\n",
      " |  \n",
      " |  __iand__(self, bs)\n",
      " |  \n",
      " |  __ilshift__(self, n)\n",
      " |      Shift bits by n to the left in place. Return self.\n",
      " |      \n",
      " |      n -- the number of bits to shift. Must be >= 0.\n",
      " |  \n",
      " |  __imul__(self, n)\n",
      " |      Concatenate n copies of self in place. Return self.\n",
      " |      \n",
      " |      Called for expressions of the form 'a *= 3'.\n",
      " |      n -- The number of concatenations. Must be >= 0.\n",
      " |  \n",
      " |  __init__(self, auto=None, length=None, offset=None, **kwargs)\n",
      " |      Either specify an 'auto' initialiser:\n",
      " |      auto -- a string of comma separated tokens, an integer, a file object,\n",
      " |              a bytearray, a boolean iterable or another bitstring.\n",
      " |      \n",
      " |      Or initialise via **kwargs with one (and only one) of:\n",
      " |      bytes -- raw data as a string, for example read from a binary file.\n",
      " |      bin -- binary string representation, e.g. '0b001010'.\n",
      " |      hex -- hexadecimal string representation, e.g. '0x2ef'\n",
      " |      oct -- octal string representation, e.g. '0o777'.\n",
      " |      uint -- an unsigned integer.\n",
      " |      int -- a signed integer.\n",
      " |      float -- a floating point number.\n",
      " |      uintbe -- an unsigned big-endian whole byte integer.\n",
      " |      intbe -- a signed big-endian whole byte integer.\n",
      " |      floatbe - a big-endian floating point number.\n",
      " |      uintle -- an unsigned little-endian whole byte integer.\n",
      " |      intle -- a signed little-endian whole byte integer.\n",
      " |      floatle -- a little-endian floating point number.\n",
      " |      uintne -- an unsigned native-endian whole byte integer.\n",
      " |      intne -- a signed native-endian whole byte integer.\n",
      " |      floatne -- a native-endian floating point number.\n",
      " |      se -- a signed exponential-Golomb code.\n",
      " |      ue -- an unsigned exponential-Golomb code.\n",
      " |      sie -- a signed interleaved exponential-Golomb code.\n",
      " |      uie -- an unsigned interleaved exponential-Golomb code.\n",
      " |      bool -- a boolean (True or False).\n",
      " |      filename -- a file which will be opened in binary read-only mode.\n",
      " |      \n",
      " |      Other keyword arguments:\n",
      " |      length -- length of the bitstring in bits, if needed and appropriate.\n",
      " |                It must be supplied for all integer and float initialisers.\n",
      " |      offset -- bit offset to the data. These offset bits are\n",
      " |                ignored and this is intended for use when\n",
      " |                initialising using 'bytes' or 'filename'.\n",
      " |  \n",
      " |  __ior__(self, bs)\n",
      " |  \n",
      " |  __irshift__(self, n)\n",
      " |      Shift bits by n to the right in place. Return self.\n",
      " |      \n",
      " |      n -- the number of bits to shift. Must be >= 0.\n",
      " |  \n",
      " |  __ixor__(self, bs)\n",
      " |  \n",
      " |  __setitem__(self, key, value)\n",
      " |  \n",
      " |  append(self, bs)\n",
      " |      Append a bitstring to the current bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to append.\n",
      " |  \n",
      " |  byteswap(self, fmt=None, start=None, end=None, repeat=True)\n",
      " |      Change the endianness in-place. Return number of repeats of fmt done.\n",
      " |      \n",
      " |      fmt -- A compact structure string, an integer number of bytes or\n",
      " |             an iterable of integers. Defaults to 0, which byte reverses the\n",
      " |             whole bitstring.\n",
      " |      start -- Start bit position, defaults to 0.\n",
      " |      end -- End bit position, defaults to self.len.\n",
      " |      repeat -- If True (the default) the byte swapping pattern is repeated\n",
      " |                as much as possible.\n",
      " |  \n",
      " |  clear(self)\n",
      " |      Remove all bits, reset to zero length.\n",
      " |  \n",
      " |  copy(self)\n",
      " |      Return a copy of the bitstring.\n",
      " |  \n",
      " |  insert(self, bs, pos=None)\n",
      " |      Insert bs at bit position pos.\n",
      " |      \n",
      " |      bs -- The bitstring to insert.\n",
      " |      pos -- The bit position to insert at.\n",
      " |      \n",
      " |      Raises ValueError if pos < 0 or pos > self.len.\n",
      " |  \n",
      " |  invert(self, pos=None)\n",
      " |      Invert one or many bits from 0 to 1 or vice versa.\n",
      " |      \n",
      " |      pos -- Either a single bit position or an iterable of bit positions.\n",
      " |             Negative numbers are treated in the same way as slice indices.\n",
      " |      \n",
      " |      Raises IndexError if pos < -self.len or pos >= self.len.\n",
      " |  \n",
      " |  overwrite(self, bs, pos=None)\n",
      " |      Overwrite with bs at bit position pos.\n",
      " |      \n",
      " |      bs -- The bitstring to overwrite with.\n",
      " |      pos -- The bit position to begin overwriting from.\n",
      " |      \n",
      " |      Raises ValueError if pos < 0 or pos + bs.len > self.len\n",
      " |  \n",
      " |  prepend(self, bs)\n",
      " |      Prepend a bitstring to the current bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to prepend.\n",
      " |  \n",
      " |  replace(self, old, new, start=None, end=None, count=None, bytealigned=None)\n",
      " |      Replace all occurrences of old with new in place.\n",
      " |      \n",
      " |      Returns number of replacements made.\n",
      " |      \n",
      " |      old -- The bitstring to replace.\n",
      " |      new -- The replacement bitstring.\n",
      " |      start -- Any occurrences that start before this will not be replaced.\n",
      " |               Defaults to 0.\n",
      " |      end -- Any occurrences that finish after this will not be replaced.\n",
      " |             Defaults to self.len.\n",
      " |      count -- The maximum number of replacements to make. Defaults to\n",
      " |               replace all occurrences.\n",
      " |      bytealigned -- If True replacements will only be made on byte\n",
      " |                     boundaries.\n",
      " |      \n",
      " |      Raises ValueError if old is empty or if start or end are\n",
      " |      out of range.\n",
      " |  \n",
      " |  reverse(self, start=None, end=None)\n",
      " |      Reverse bits in-place.\n",
      " |      \n",
      " |      start -- Position of first bit to reverse. Defaults to 0.\n",
      " |      end -- One past the position of the last bit to reverse.\n",
      " |             Defaults to self.len.\n",
      " |      \n",
      " |      Using on an empty bitstring will have no effect.\n",
      " |      \n",
      " |      Raises ValueError if start < 0, end > self.len or end < start.\n",
      " |  \n",
      " |  rol(self, bits, start=None, end=None)\n",
      " |      Rotate bits to the left in-place.\n",
      " |      \n",
      " |      bits -- The number of bits to rotate by.\n",
      " |      start -- Start of slice to rotate. Defaults to 0.\n",
      " |      end -- End of slice to rotate. Defaults to self.len.\n",
      " |      \n",
      " |      Raises ValueError if bits < 0.\n",
      " |  \n",
      " |  ror(self, bits, start=None, end=None)\n",
      " |      Rotate bits to the right in-place.\n",
      " |      \n",
      " |      bits -- The number of bits to rotate by.\n",
      " |      start -- Start of slice to rotate. Defaults to 0.\n",
      " |      end -- End of slice to rotate. Defaults to self.len.\n",
      " |      \n",
      " |      Raises ValueError if bits < 0.\n",
      " |  \n",
      " |  set(self, value, pos=None)\n",
      " |      Set one or many bits to 1 or 0.\n",
      " |      \n",
      " |      value -- If True bits are set to 1, otherwise they are set to 0.\n",
      " |      pos -- Either a single bit position or an iterable of bit positions.\n",
      " |             Negative numbers are treated in the same way as slice indices.\n",
      " |             Defaults to the entire bitstring.\n",
      " |      \n",
      " |      Raises IndexError if pos < -self.len or pos >= self.len.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(cls, auto=None, length=None, offset=None, **kwargs)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  bin\n",
      " |      The bitstring as a binary string. Read and write.\n",
      " |  \n",
      " |  bool\n",
      " |      The bitstring as a bool (True or False). Read and write.\n",
      " |  \n",
      " |  bytes\n",
      " |      The bitstring as a ordinary string. Read and write.\n",
      " |  \n",
      " |  float\n",
      " |      The bitstring as a floating point number. Read and write.\n",
      " |  \n",
      " |  floatbe\n",
      " |      The bitstring as a big-endian floating point number. Read and write.\n",
      " |  \n",
      " |  floatle\n",
      " |      The bitstring as a little-endian floating point number. Read and write.\n",
      " |  \n",
      " |  floatne\n",
      " |      The bitstring as a native-endian floating point number. Read and write.\n",
      " |  \n",
      " |  hex\n",
      " |      The bitstring as a hexadecimal string. Read and write.\n",
      " |  \n",
      " |  int\n",
      " |      The bitstring as a two's complement signed int. Read and write.\n",
      " |  \n",
      " |  intbe\n",
      " |      The bitstring as a two's complement big-endian signed int. Read and write.\n",
      " |  \n",
      " |  intle\n",
      " |      The bitstring as a two's complement little-endian signed int. Read and write.\n",
      " |  \n",
      " |  intne\n",
      " |      The bitstring as a two's complement native-endian signed int. Read and write.\n",
      " |  \n",
      " |  oct\n",
      " |      The bitstring as an octal string. Read and write.\n",
      " |  \n",
      " |  se\n",
      " |      The bitstring as a signed exponential-Golomb code. Read and write.\n",
      " |  \n",
      " |  sie\n",
      " |      The bitstring as a signed interleaved exponential-Golomb code. Read and write.\n",
      " |  \n",
      " |  ue\n",
      " |      The bitstring as an unsigned exponential-Golomb code. Read and write.\n",
      " |  \n",
      " |  uie\n",
      " |      The bitstring as an unsigned interleaved exponential-Golomb code. Read and write.\n",
      " |  \n",
      " |  uint\n",
      " |      The bitstring as a two's complement unsigned int. Read and write.\n",
      " |  \n",
      " |  uintbe\n",
      " |      The bitstring as a two's complement big-endian unsigned int. Read and write.\n",
      " |  \n",
      " |  uintle\n",
      " |      The bitstring as a two's complement little-endian unsigned int. Read and write.\n",
      " |  \n",
      " |  uintne\n",
      " |      The bitstring as a two's complement native-endian unsigned int. Read and write.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from Bits:\n",
      " |  \n",
      " |  __add__(self, bs)\n",
      " |      Concatenate bitstrings and return new bitstring.\n",
      " |      \n",
      " |      bs -- the bitstring to append.\n",
      " |  \n",
      " |  __and__(self, bs)\n",
      " |      Bit-wise 'and' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to '&' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  __bool__ = __nonzero__(self)\n",
      " |  \n",
      " |  __contains__(self, bs)\n",
      " |      Return whether bs is contained in the current bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to search for.\n",
      " |  \n",
      " |  __eq__(self, bs)\n",
      " |      Return True if two bitstrings have the same binary representation.\n",
      " |      \n",
      " |      >>> BitArray('0b1110') == '0xe'\n",
      " |      True\n",
      " |  \n",
      " |  __ge__(self, other)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getitem__(self, key)\n",
      " |      Return a new bitstring representing a slice of the current bitstring.\n",
      " |      \n",
      " |      Indices are in units of the step parameter (default 1 bit).\n",
      " |      Stepping is used to specify the number of bits in each item.\n",
      " |      \n",
      " |      >>> print BitArray('0b00110')[1:4]\n",
      " |      '0b011'\n",
      " |      >>> print BitArray('0x00112233')[1:3:8]\n",
      " |      '0x1122'\n",
      " |  \n",
      " |  __gt__(self, other)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __invert__(self)\n",
      " |      Return bitstring with every bit inverted.\n",
      " |      \n",
      " |      Raises Error if the bitstring is empty.\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  __le__(self, other)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |      Return the length of the bitstring in bits.\n",
      " |  \n",
      " |  __lshift__(self, n)\n",
      " |      Return bitstring with bits shifted by n to the left.\n",
      " |      \n",
      " |      n -- the number of bits to shift. Must be >= 0.\n",
      " |  \n",
      " |  __lt__(self, other)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mul__(self, n)\n",
      " |      Return bitstring consisting of n concatenations of self.\n",
      " |      \n",
      " |      Called for expression of the form 'a = b*3'.\n",
      " |      n -- The number of concatenations. Must be >= 0.\n",
      " |  \n",
      " |  __ne__(self, bs)\n",
      " |      Return False if two bitstrings have the same binary representation.\n",
      " |      \n",
      " |      >>> BitArray('0b111') == '0x7'\n",
      " |      False\n",
      " |  \n",
      " |  __nonzero__(self)\n",
      " |      Return True if any bits are set to 1, otherwise return False.\n",
      " |  \n",
      " |  __or__(self, bs)\n",
      " |      Bit-wise 'or' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to '|' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  __radd__(self, bs)\n",
      " |      Append current bitstring to bs and return new bitstring.\n",
      " |      \n",
      " |      bs -- the string for the 'auto' initialiser that will be appended to.\n",
      " |  \n",
      " |  __rand__(self, bs)\n",
      " |      Bit-wise 'and' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- the bitstring to '&' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return representation that could be used to recreate the bitstring.\n",
      " |      \n",
      " |      If the returned string is too long it will be truncated. See __str__().\n",
      " |  \n",
      " |  __rmul__(self, n)\n",
      " |      Return bitstring consisting of n concatenations of self.\n",
      " |      \n",
      " |      Called for expressions of the form 'a = 3*b'.\n",
      " |      n -- The number of concatenations. Must be >= 0.\n",
      " |  \n",
      " |  __ror__(self, bs)\n",
      " |      Bit-wise 'or' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to '|' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  __rshift__(self, n)\n",
      " |      Return bitstring with bits shifted by n to the right.\n",
      " |      \n",
      " |      n -- the number of bits to shift. Must be >= 0.\n",
      " |  \n",
      " |  __rxor__(self, bs)\n",
      " |      Bit-wise 'xor' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to '^' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      Return approximate string representation of bitstring for printing.\n",
      " |      \n",
      " |      Short strings will be given wholly in hexadecimal or binary. Longer\n",
      " |      strings may be part hexadecimal and part binary. Very long strings will\n",
      " |      be truncated with '...'.\n",
      " |  \n",
      " |  __xor__(self, bs)\n",
      " |      Bit-wise 'xor' between two bitstrings. Returns new bitstring.\n",
      " |      \n",
      " |      bs -- The bitstring to '^' with.\n",
      " |      \n",
      " |      Raises ValueError if the two bitstrings have differing lengths.\n",
      " |  \n",
      " |  all(self, value, pos=None)\n",
      " |      Return True if one or many bits are all set to value.\n",
      " |      \n",
      " |      value -- If value is True then checks for bits set to 1, otherwise\n",
      " |               checks for bits set to 0.\n",
      " |      pos -- An iterable of bit positions. Negative numbers are treated in\n",
      " |             the same way as slice indices. Defaults to the whole bitstring.\n",
      " |  \n",
      " |  any(self, value, pos=None)\n",
      " |      Return True if any of one or many bits are set to value.\n",
      " |      \n",
      " |      value -- If value is True then checks for bits set to 1, otherwise\n",
      " |               checks for bits set to 0.\n",
      " |      pos -- An iterable of bit positions. Negative numbers are treated in\n",
      " |             the same way as slice indices. Defaults to the whole bitstring.\n",
      " |  \n",
      " |  count(self, value)\n",
      " |      Return count of total number of either zero or one bits.\n",
      " |      \n",
      " |      value -- If True then bits set to 1 are counted, otherwise bits set\n",
      " |               to 0 are counted.\n",
      " |      \n",
      " |      >>> Bits('0xef').count(1)\n",
      " |      7\n",
      " |  \n",
      " |  cut(self, bits, start=None, end=None, count=None)\n",
      " |      Return bitstring generator by cutting into bits sized chunks.\n",
      " |      \n",
      " |      bits -- The size in bits of the bitstring chunks to generate.\n",
      " |      start -- The bit position to start the first cut. Defaults to 0.\n",
      " |      end -- The bit position one past the last bit to use in the cut.\n",
      " |             Defaults to self.len.\n",
      " |      count -- If specified then at most count items are generated.\n",
      " |               Default is to cut as many times as possible.\n",
      " |  \n",
      " |  endswith(self, suffix, start=None, end=None)\n",
      " |      Return whether the current bitstring ends with suffix.\n",
      " |      \n",
      " |      suffix -- The bitstring to search for.\n",
      " |      start -- The bit position to start from. Defaults to 0.\n",
      " |      end -- The bit position to end at. Defaults to self.len.\n",
      " |  \n",
      " |  find = _find_msb0(self, bs, start=None, end=None, bytealigned=None)\n",
      " |  \n",
      " |  findall(self, bs, start=None, end=None, count=None, bytealigned=None)\n",
      " |      Find all occurrences of bs. Return generator of bit positions.\n",
      " |      \n",
      " |      bs -- The bitstring to find.\n",
      " |      start -- The bit position to start the search. Defaults to 0.\n",
      " |      end -- The bit position one past the last bit to search.\n",
      " |             Defaults to self.len.\n",
      " |      count -- The maximum number of occurrences to find.\n",
      " |      bytealigned -- If True the bitstring will only be found on\n",
      " |                     byte boundaries.\n",
      " |      \n",
      " |      Raises ValueError if bs is empty, if start < 0, if end > self.len or\n",
      " |      if end < start.\n",
      " |      \n",
      " |      Note that all occurrences of bs are found, even if they overlap.\n",
      " |  \n",
      " |  join(self, sequence)\n",
      " |      Return concatenation of bitstrings joined by self.\n",
      " |      \n",
      " |      sequence -- A sequence of bitstrings.\n",
      " |  \n",
      " |  rfind(self, bs, start=None, end=None, bytealigned=None)\n",
      " |      Find final occurrence of substring bs.\n",
      " |      \n",
      " |      Returns a single item tuple with the bit position if found, or an\n",
      " |      empty tuple if not found. The bit position (pos property) will\n",
      " |      also be set to the start of the substring if it is found.\n",
      " |      \n",
      " |      bs -- The bitstring to find.\n",
      " |      start -- The bit position to end the reverse search. Defaults to 0.\n",
      " |      end -- The bit position one past the first bit to reverse search.\n",
      " |             Defaults to self.len.\n",
      " |      bytealigned -- If True the bitstring will only be found on byte\n",
      " |                     boundaries.\n",
      " |      \n",
      " |      Raises ValueError if bs is empty, if start < 0, if end > self.len or\n",
      " |      if end < start.\n",
      " |  \n",
      " |  split(self, delimiter, start=None, end=None, count=None, bytealigned=None)\n",
      " |      Return bitstring generator by splittling using a delimiter.\n",
      " |      \n",
      " |      The first item returned is the initial bitstring before the delimiter,\n",
      " |      which may be an empty bitstring.\n",
      " |      \n",
      " |      delimiter -- The bitstring used as the divider.\n",
      " |      start -- The bit position to start the split. Defaults to 0.\n",
      " |      end -- The bit position one past the last bit to use in the split.\n",
      " |             Defaults to self.len.\n",
      " |      count -- If specified then at most count items are generated.\n",
      " |               Default is to split as many times as possible.\n",
      " |      bytealigned -- If True splits will only occur on byte boundaries.\n",
      " |      \n",
      " |      Raises ValueError if the delimiter is empty.\n",
      " |  \n",
      " |  startswith(self, prefix, start=None, end=None)\n",
      " |      Return whether the current bitstring starts with prefix.\n",
      " |      \n",
      " |      prefix -- The bitstring to search for.\n",
      " |      start -- The bit position to start from. Defaults to 0.\n",
      " |      end -- The bit position to end at. Defaults to self.len.\n",
      " |  \n",
      " |  tobytes(self)\n",
      " |      Return the bitstring as bytes, padding with zero bits if needed.\n",
      " |      \n",
      " |      Up to seven zero bits will be added at the end to byte align.\n",
      " |  \n",
      " |  tofile(self, f)\n",
      " |      Write the bitstring to a file object, padding with zero bits if needed.\n",
      " |      \n",
      " |      Up to seven zero bits will be added at the end to byte align.\n",
      " |  \n",
      " |  unpack(self, fmt, **kwargs)\n",
      " |      Interpret the whole bitstring using fmt and return list.\n",
      " |      \n",
      " |      fmt -- A single string or a list of strings with comma separated tokens\n",
      " |             describing how to interpret the bits in the bitstring. Items\n",
      " |             can also be integers, for reading new bitstring of the given length.\n",
      " |      kwargs -- A dictionary or keyword-value pairs - the keywords used in the\n",
      " |                format string will be replaced with their given value.\n",
      " |      \n",
      " |      Raises ValueError if the format is not understood. If not enough bits\n",
      " |      are available then all bits to the end of the bitstring will be used.\n",
      " |      \n",
      " |      See the docstring for 'read' for token examples.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from Bits:\n",
      " |  \n",
      " |  len\n",
      " |      The length of the bitstring in bits. Read only.\n",
      " |  \n",
      " |  length\n",
      " |      The length of the bitstring in bits. Read only.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(bitstring.BitArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ba = bitstring.BitArray( \"Bryce\".encode(\"utf-8\") )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__and__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ilshift__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__irshift__',\n",
       " '__iter__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__or__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__rshift__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__slots__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__xor__',\n",
       " '_append',\n",
       " '_assertsanity',\n",
       " '_clear',\n",
       " '_converttobitstring',\n",
       " '_copy',\n",
       " '_datastore',\n",
       " '_delete',\n",
       " '_delete_lsb0',\n",
       " '_delete_msb0',\n",
       " '_ensureinmemory',\n",
       " '_find_lsb0',\n",
       " '_find_msb0',\n",
       " '_findbytes',\n",
       " '_findregex',\n",
       " '_getbin',\n",
       " '_getbool',\n",
       " '_getbytes',\n",
       " '_getfloat',\n",
       " '_getfloatle',\n",
       " '_getfloatne',\n",
       " '_gethex',\n",
       " '_getint',\n",
       " '_getintbe',\n",
       " '_getintle',\n",
       " '_getintne',\n",
       " '_getlength',\n",
       " '_getoct',\n",
       " '_getoffset',\n",
       " '_getse',\n",
       " '_getsie',\n",
       " '_getue',\n",
       " '_getuie',\n",
       " '_getuint',\n",
       " '_getuintbe',\n",
       " '_getuintle',\n",
       " '_getuintne',\n",
       " '_iand',\n",
       " '_ilshift',\n",
       " '_imul',\n",
       " '_init_with_token',\n",
       " '_initialise',\n",
       " '_initialise_from_auto',\n",
       " '_inplace_logical_helper',\n",
       " '_insert',\n",
       " '_insert_lsb0',\n",
       " '_insert_msb0',\n",
       " '_invert',\n",
       " '_invert_all',\n",
       " '_ior',\n",
       " '_irshift',\n",
       " '_ixor',\n",
       " '_offset',\n",
       " '_overwrite',\n",
       " '_overwrite_lsb0',\n",
       " '_overwrite_msb0',\n",
       " '_prepend',\n",
       " '_readbin',\n",
       " '_readbits',\n",
       " '_readbool',\n",
       " '_readbytes',\n",
       " '_readfloat',\n",
       " '_readfloatle',\n",
       " '_readfloatne',\n",
       " '_readhex',\n",
       " '_readint',\n",
       " '_readintbe',\n",
       " '_readintle',\n",
       " '_readintne',\n",
       " '_readlist',\n",
       " '_readoct',\n",
       " '_readse',\n",
       " '_readsie',\n",
       " '_readtoken',\n",
       " '_readue',\n",
       " '_readuie',\n",
       " '_readuint',\n",
       " '_readuintbe',\n",
       " '_readuintle',\n",
       " '_readuintne',\n",
       " '_reverse',\n",
       " '_reversebytes',\n",
       " '_set',\n",
       " '_setauto',\n",
       " '_setbin_safe',\n",
       " '_setbin_unsafe',\n",
       " '_setbool',\n",
       " '_setbytes_safe',\n",
       " '_setbytes_unsafe',\n",
       " '_setfile',\n",
       " '_setfloat',\n",
       " '_setfloatle',\n",
       " '_setfloatne',\n",
       " '_sethex',\n",
       " '_setint',\n",
       " '_setintbe',\n",
       " '_setintle',\n",
       " '_setintne',\n",
       " '_setoct',\n",
       " '_setse',\n",
       " '_setsie',\n",
       " '_setue',\n",
       " '_setuie',\n",
       " '_setuint',\n",
       " '_setuintbe',\n",
       " '_setuintle',\n",
       " '_setuintne',\n",
       " '_slice',\n",
       " '_slice_lsb0',\n",
       " '_slice_msb0',\n",
       " '_truncateleft',\n",
       " '_truncateright',\n",
       " '_unset',\n",
       " '_validate_slice',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'bin',\n",
       " 'bool',\n",
       " 'bytes',\n",
       " 'byteswap',\n",
       " 'clear',\n",
       " 'copy',\n",
       " 'count',\n",
       " 'cut',\n",
       " 'endswith',\n",
       " 'find',\n",
       " 'findall',\n",
       " 'float',\n",
       " 'floatbe',\n",
       " 'floatle',\n",
       " 'floatne',\n",
       " 'hex',\n",
       " 'insert',\n",
       " 'int',\n",
       " 'intbe',\n",
       " 'intle',\n",
       " 'intne',\n",
       " 'invert',\n",
       " 'join',\n",
       " 'len',\n",
       " 'length',\n",
       " 'oct',\n",
       " 'overwrite',\n",
       " 'prepend',\n",
       " 'replace',\n",
       " 'reverse',\n",
       " 'rfind',\n",
       " 'rol',\n",
       " 'ror',\n",
       " 'se',\n",
       " 'set',\n",
       " 'sie',\n",
       " 'split',\n",
       " 'startswith',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'ue',\n",
       " 'uie',\n",
       " 'uint',\n",
       " 'uintbe',\n",
       " 'uintle',\n",
       " 'uintne',\n",
       " 'unpack']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for bit in string_to_binary(\"Bryce\"):\n",
    "    print(bit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def string_to_binary(s):\n",
    "    \n",
    "    #return bitstring.BitArray( s.encode(\"utf-8\") )"
   ]
  }
 ],
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