{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netzob.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x0c'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = Integer(value=12, unitSize=UnitSize.SIZE_8)\n",
    "i.generate().tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x00\\x00\\x00\\x0c'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = Integer(value=12, unitSize=UnitSize.SIZE_32)\n",
    "i.generate().tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x0c\\x00\\x00\\x00'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = Integer(value=12, unitSize=UnitSize.SIZE_32, endianness=Endianness.LITTLE)\n",
    "i.generate().tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xff\\xf4'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = Integer(value=-12, sign=Sign.SIGNED, unitSize=UnitSize.SIZE_16)\n",
    "i.generate().tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bitarray('00000000000000000000010011010010')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int32be(1234).value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x00\\x00\\x04\\xd2'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int32be(1234).value.tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = Raw(nbBytes=6)\n",
    "len(r.generate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(r.generate().tobytes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{111, 116}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = Raw(nbBytes=100, alphabet=[b\"t\", b\"o\"])\n",
    "data = r.generate().tobytes()\n",
    "data_set = set(data)\n",
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fheader"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fh0 = Field(name='fh0')\n",
    "fh1 = Field(name='fh1')\n",
    "fheader = Field([fh0, fh1], name='fheader')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abstract 和 Preset 功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建规则\n",
    "f1 = Field(name=\"name\", domain=\"john\")\n",
    "f2 = Field(name=\"question\", domain=\", what's up in \")\n",
    "f3 = Field(name=\"city\", domain=Alt([\"Paris\", \"Berlin\"]))\n",
    "f4 = Field(name=\"mark\", domain=\" ?\")\n",
    "symbol = Symbol([f1, f2, f3, f4], name=\"Symbol-john\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置预测约束（Setting the Preset）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "preset = Preset(symbol)\n",
    "preset[f3] = b\"Paris\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提供数据进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AbstractionException",
     "evalue": "With the symbol/field 'Symbol-john', can abstract the data: 'john, what's up in Berlin ?', but some parsed values do not match the expected preset.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAbstractionException\u001b[0m                      Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjohn, what\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms up in Berlin ?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m data_structure \u001b[38;5;241m=\u001b[39m \u001b[43msymbol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabstract\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreset\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/CCSDS_study/ccsds/lib/python3.8/site-packages/netzob/Model/Vocabulary/AbstractField.py:628\u001b[0m, in \u001b[0;36mAbstractField.abstract\u001b[0;34m(self, data, preset, memory)\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m data_structure\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 628\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m AbstractionException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWith the symbol/field \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, can abstract the data: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, but some parsed values do not match the expected preset.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m, data)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParsingPathException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    630\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m AbstractionException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWith the symbol/field \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, cannot abstract the data: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Error: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m, data, e)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mAbstractionException\u001b[0m: With the symbol/field 'Symbol-john', can abstract the data: 'john, what's up in Berlin ?', but some parsed values do not match the expected preset."
     ]
    }
   ],
   "source": [
    "data = \"john, what's up in Berlin ?\"\n",
    "data_structure = symbol.abstract(data, preset=preset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('name', b'john'),\n",
       "             ('question', b\", what's up in \"),\n",
       "             ('city', b'Paris'),\n",
       "             ('mark', b' ?')])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = \"john, what's up in Paris ?\"\n",
    "data_structure = symbol.abstract(data, preset=preset)\n",
    "data_structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ccsds",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
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