09_SyntaxLearn_Function_2_Addition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43339155-a82d-49bd-bc50-ce5b5af81280",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Lambda لامبدا"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0aa999-0a7d-444f-a6c6-04d94f34494d",
   "metadata": {},
   "source": [
    "از این توابع کوچک برای مپینگ و فیلتر زیاد استفاده می شود"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88c1edb-4c91-420a-9e77-fc6e96022b4a",
   "metadata": {},
   "source": [
    "خود به خود عبارتی را برگشت میدهند"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16663e8e-b5c6-4fd8-aecf-d159aedbdb9a",
   "metadata": {},
   "source": [
    "هرچنتا میتونن ورودی بگیرن اما بدنه آنها یک عبارت دارد"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d709de-7ed6-4861-8784-406a52a4fbc9",
   "metadata": {},
   "source": [
    "برای توابع کوچک و یک خطی از توابع لامبدا استفاده می شود که ناشناس و یکبارمصرف هستند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23b37612-e17b-4ba5-9e9c-e2a964c72686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "f = lambda x: x**2  # ابتدا کلمه کلیدی لامبدا، بعد ورودی ها و بعد دونقطه محاسبه. ریترن نمی \n",
    "print(f(5))\n",
    "# در لامبدا نمی توان حلقه نوشت"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1bcc217-4f77-4123-b98a-c54164d9cd98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<map object at 0x7f7d60c5ed10>\n",
      "[1, 4, 9, 16]\n",
      "[1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "# map\n",
    "# map(func, list)\n",
    "# عناصر لیست را به تابع میفرست و یک لیست جدید از نوع شیء مپ خروجی می دهد\n",
    "\n",
    "def func(x):\n",
    "    return x**2\n",
    "\n",
    "li = [1, 2, 3, 4]\n",
    "newli = map(lambda x: x**2, li)\n",
    "\n",
    "newli2 = map(func, li)\n",
    "\n",
    "print(newli) #شیء مپ را چاپ می کند\n",
    "print(list(newli))\n",
    "print(list(newli2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cee1a197-b796-4518-bc6f-c303e2617692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "# filter\n",
    "# filter(func, list)\n",
    "# عناصر لیست را به تابع می فرستد براساس ترو یا فالس بودن نگه میدارد و به عنوان خروجی برمیگرداند\n",
    "\n",
    "li = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "newli = list(filter(lambda x: x>5, li))\n",
    "print(newli)\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5629edbf-db78-4457-8a97-71a2e81d5955",
   "metadata": {},
   "source": [
    "![image.png](attachment:55d3ee31-38a1-4fd0-9341-c4997e385a30.png)  reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e07cdc2-96fb-4a89-9fe4-37cd23140821",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n"
     ]
    }
   ],
   "source": [
    "# reduce\n",
    "# reduce(func, list)\n",
    "# نیازمند ایمپورت هست\n",
    "\n",
    "from functools import reduce\n",
    "\n",
    "li = [1, 2, 3, 4, 5]\n",
    "result = reduce(lambda x,y: x*y, li) # فاکتوریل\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c464d40-00d3-4a16-aa49-fc69d3fc3c67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 9, 4, 1, 4, 7, 1, 25, 2, 8]\n",
      "['r34', 'asas', '34r34r345']\n"
     ]
    }
   ],
   "source": [
    "# کلید تابع sorted\n",
    "# sorted(li, key = func)\n",
    "li = [4, 1, 2, 3, 4, 8, 7, 1, 25, 9]\n",
    "li1 = [\"asas\" , \"r34\" , \"34r34r345\"]\n",
    "newli1 = sorted(li1, key = len)  \n",
    "newli = sorted(li, key = lambda x: x%3)  # همان لیست بر میگرداند نه شیء دیگر\n",
    "print(newli)\n",
    "print(newli1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bb79d83-3000-4c7f-9830-a0f57bb0388e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "s = \"ali\"\n",
    "start_with = lambda x: True if x.startswith(\"a\") else False\n",
    "\n",
    "print(start_with(\"a\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06a71932-9435-4dd4-9335-a9f0f77f20f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "is_num = lambda s: s.replace(\".\" , \"\" , 1).isdigit()\n",
    "\n",
    "print(is_num('32'))\n",
    "print(is_num('3.2'))\n",
    "print(is_num('asad'))\n",
    "print(is_num('3a2'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab0c64c",
   "metadata": {},
   "source": [
    "# Iterator ایتریتور"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf0006b",
   "metadata": {},
   "source": [
    "Iteration : While و For عمل تکرار را انجام می دهند"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8ace0e",
   "metadata": {},
   "source": [
    "Iterable : تمام آبجکت هایی که روی آنها می توانیم عمل تکرار را انجام دهیم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3d63f3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3]\n",
    "print(dir(l))  # اتریبیوت های آبجکت را چاپ می کند\n",
    "print(\"__iter__\" in dir(l))  # بررسی وجود صفت تکرارپذیر در صفات لیست\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c01b374",
   "metadata": {},
   "source": [
    "Iterator : تکرارگر ها آبجکت های ایتریبل هستند که آخرین وضعیت خود را حفظ می کنند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6431c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "1\n",
      "2\n",
      "6\n",
      "4\n"
     ]
    },
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 14>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mnext\u001b[39m(newlist) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mnext\u001b[39m(newlist))\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnewlist\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[1;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# بتوانیم در آن ها متد نکست را اجرا کنیم Next\n",
    "i = [1, 2, 3, 4]\n",
    "print(\"__next__\" in dir(i))  # ایتریتور نیست\n",
    "\n",
    "newlist = iter(i)  # این تابع تبدیل به ایتریتور می کند\n",
    "print(\"__next__\" in dir(newlist))\n",
    "# i.__iter__()  # روش دیگر برای فعال کردن ایتریتور در آبجکت\n",
    "\n",
    "print(next(newlist))\n",
    "print(newlist.__next__())  # شکل دیگر پیمایش\n",
    "print(next(newlist) * 2)\n",
    "print(next(newlist))\n",
    "\n",
    "print(next(newlist))  # در پایان ارور می دهد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2073f071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "******************************\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# مثال\n",
    "l = iter([1, 2, 3, 4])\n",
    "while True:\n",
    "    try:\n",
    "        print(next(l))\n",
    "    except StopIteration:\n",
    "        break\n",
    "\n",
    "print(\"*\"*30)\n",
    "l2 = [1 ,2, 3]\n",
    "for i in l2:\n",
    "    print(i)\n",
    "    \n",
    "# در واقع فوور ابجکت را به ایتریتور تبدیل میکنند\n",
    "# tryExcept در فصل های آینده بررسی خواهد شد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d54c92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "******************************\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "# ایتریتور ها می توانند بدون پایان باشند\n",
    "from itertools import count\n",
    "\n",
    "counter = count()  # داخل  پرانتز عدد شروع نوشته می شود\n",
    "for i in range(4):\n",
    "    print(next(counter))\n",
    "print(\"*\"*30)\n",
    "counter2 = count(21)  # داخل  پرانتز عدد شروع نوشته می شود\n",
    "print(next(counter2))\n",
    "print(next(counter2))\n",
    "print(next(counter2))\n",
    "print(next(counter2))\n",
    "print(next(counter2))\n",
    "\n",
    "# count لِیزی هست\n",
    "# لیزی در دنیای برنامه نویسی خوبه و یعنی تا نیاز نباشه استفاده نمیشه\n",
    "# در واقع اگر کانتر را در حلق هم بندازیم اینطور نیست که از اول بی نهایت باشه در هر چرخه هر بار نیاز باشد عدد جدید می دهد"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb6e863",
   "metadata": {},
   "source": [
    "# Decorator دکوراتور"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e3f9f4",
   "metadata": {},
   "source": [
    "دکوراتور بیلت این پایتون نیست بلکه ابزاری است که تعریف می کنیم"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6996b46",
   "metadata": {},
   "source": [
    "دکوراتور یک تابع یا کلاس را گرفته و به شکل ویرایش شده یا تزیین شده بر می گرداند"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514503b5-0f0d-44ee-8f8d-e9b21e420a0a",
   "metadata": {},
   "source": [
    "                              توابع تو در تو \n",
    "ارسال تابع بعنوان ارگومان"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97d9a02",
   "metadata": {},
   "source": [
    "نام دیگر دکوراتور wrapper"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "66c2f197",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d798fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reza\n",
      "********************\n",
      "reza\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "def dec(func):\n",
    "    def inner():\n",
    "        print('*' * 20)\n",
    "        func()\n",
    "        print('*' * 20)\n",
    "    return inner\n",
    "\n",
    "def f():\n",
    "    print('reza')\n",
    "\n",
    "f()  # حالت عادی تابع\n",
    "\n",
    "dfunc = dec(f)  # حالت دکور شده تابع\n",
    "dfunc()\n",
    "# یک تابع را ویژگی هایش را بیشتر میکند\n",
    "# توجه شود که همه آبجکت ها از جمله توابع تعریفی در پایتون فرست کلاس هستند و امکان ارسال به عنوان آرگومان وجود دارد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44656fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "reza\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "# در صورتی که بخواهیم تابع هنگام تعریف دکور شود می توان از روش زیر هنگام تعریف استفاده کرد\n",
    "\n",
    "def dec(func):\n",
    "    def inner():\n",
    "        print('*' * 20)\n",
    "        func()\n",
    "        print('*' * 20)\n",
    "    return inner\n",
    "\n",
    "@dec\n",
    "def f():\n",
    "    print('reza')\n",
    "    \n",
    "f()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c03b16a-0d81-40cd-b2b1-30f336a1eabd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n",
      "warning!!\n",
      "\n",
      "0.6666666666666666\n",
      "warning!!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def dec(func):\n",
    "    def inner(x,y):\n",
    "        if y == 0:\n",
    "            print(\"warning!!\")\n",
    "        else:\n",
    "            func(x ,y)\n",
    "    return inner\n",
    "\n",
    "@dec\n",
    "def f(x, y):\n",
    "    print((x/y))\n",
    "\n",
    "f(2, 3)\n",
    "f(2, 0)\n",
    "\n",
    "print()\n",
    "\n",
    "def dec(func):\n",
    "    def inner(x,y):\n",
    "        if y == 0:\n",
    "            return (\"warning!!\")\n",
    "        return func(x ,y)\n",
    "    return inner\n",
    "\n",
    "@dec\n",
    "def f(x, y):\n",
    "    return x/y\n",
    "\n",
    "print(f(2, 3))\n",
    "print(f(2, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa5ddb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "(2, 3, 5)\n",
      "{}\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# برای دکوراتور لازم است آرگومان های تابع در نظر گرفته شود\n",
    "# راهکار کلی به شکل زیر است\n",
    "\n",
    "def dec(func):\n",
    "    def inner(*x, **y):\n",
    "        print('*' * 20)\n",
    "        print(x)\n",
    "        print(y)\n",
    "        return func(*x, **y)\n",
    "    return inner\n",
    "\n",
    "@dec\n",
    "def f(x, y, z):\n",
    "    return(max(x,y,z))\n",
    "\n",
    "print(f(2, 3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "78541361-0dda-4a80-9f1c-3cb017e40f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "********************\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'I am Hoseyn'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def star(func):\n",
    "    def inner(*x, **y):\n",
    "        print('*' * 20)\n",
    "        f = func(*x, **y)\n",
    "        print('*' * 20)\n",
    "        return f\n",
    "    return inner\n",
    "\n",
    "@star\n",
    "def msg(name):\n",
    "    return \"I am \" + name\n",
    "    \n",
    "\n",
    "msg(\"Hoseyn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61031a42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "++++++++++++++++++++\n",
      "********************\n",
      "I am Hoseyn\n",
      "********************\n",
      "++++++++++++++++++++\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "def star(func):\n",
    "    def inner(*x, **y):\n",
    "        print('*' * 20)\n",
    "        func(*x, **y)\n",
    "        print('*' * 20)\n",
    "    return inner\n",
    "\n",
    "def plus(func):\n",
    "    def inner(*x, **y):\n",
    "        print('+' * 20)\n",
    "        func(*x, **y)\n",
    "        print('+' * 20)\n",
    "    return inner\n",
    "\n",
    "def dash(func):\n",
    "    def inner(*x, **y):\n",
    "        print('-' * 20)\n",
    "        func(*x, **y)\n",
    "        print('-' * 20)\n",
    "    return inner\n",
    "\n",
    "\n",
    "@dash\n",
    "@plus\n",
    "@star\n",
    "def msg(name):\n",
    "    print(\"I am\", name)\n",
    "    \n",
    "\n",
    "msg(\"Hoseyn\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d86c033f-57ca-4e77-adac-1a00b43fdae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "++++++++++++++++++++\n",
      "********************\n",
      "--------------------\n",
      "I am Hoseyn\n",
      "--------------------\n",
      "********************\n",
      "++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "def star(func):\n",
    "    def inner(*x, **y):\n",
    "        print('*' * 20)\n",
    "        func(*x, **y)\n",
    "        print('*' * 20)\n",
    "    return inner\n",
    "\n",
    "def plus(func):\n",
    "    def inner(*x, **y):\n",
    "        print('+' * 20)\n",
    "        func(*x, **y)\n",
    "        print('+' * 20)\n",
    "    return inner\n",
    "\n",
    "def dash(func):\n",
    "    def inner(*x, **y):\n",
    "        print('-' * 20)\n",
    "        func(*x, **y)\n",
    "        print('-' * 20)\n",
    "    return inner\n",
    "\n",
    "\n",
    "\n",
    "def msg(name):\n",
    "    print(\"I am\", name)\n",
    "    \n",
    "printer = plus(star(dash(msg)))\n",
    "printer(\"Hoseyn\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "217991fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************************************************\n",
      "I am Hoseyn\n",
      "******************************\n"
     ]
    }
   ],
   "source": [
    "# دکوراتور با ورودی\n",
    "\n",
    "def star(a, b):\n",
    "    def inner1(func):\n",
    "        def inner2(*x, **y):\n",
    "            print('*' * a)\n",
    "            func(*x, **y)\n",
    "            print('*' * b)\n",
    "        return inner2\n",
    "    return inner1\n",
    "\n",
    "@star(100, 30)\n",
    "def msg(name):\n",
    "    print(\"I am\", name)\n",
    "    \n",
    "    \n",
    "msg(\"Hoseyn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c83548f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "I am Hoseyn\n",
      "********************\n",
      "prints a message\n",
      "msg\n"
     ]
    }
   ],
   "source": [
    "# برای کپی کردن داک استرینگ و اسم تابع از روش زیر استفاده می شود\n",
    "# در حالت عادی زیرا از ی تابع دیگر برای بازنویس تابع استفاده کردیم اطلاعات تابع دیگر نوشته میشود \n",
    "import functools\n",
    "\n",
    "# با استفاده از رپس اطلاعات تابع در تابع دکراتور ککپی می شود \n",
    "def dec(func):\n",
    "    @functools.wraps(func)\n",
    "    def inner(*x, **y):\n",
    "        print('*' * 20)\n",
    "        func(*x, **y)\n",
    "        print('*' * 20)\n",
    "    return inner\n",
    "\n",
    "@dec\n",
    "def msg(name):\n",
    "    \"\"\"prints a message\"\"\"\n",
    "    print(\"I am\", name)\n",
    "    \n",
    "msg(\"Hoseyn\")\n",
    "print(msg.__doc__)\n",
    "print(msg.__name__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37049e2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ali : 23416\n",
      "This user is blocked!!!\n"
     ]
    }
   ],
   "source": [
    "# مثال از دکوراتور\n",
    "# بن لیست\n",
    "\n",
    "import functools\n",
    "\n",
    "passwords = {\"ali\":\"23416\", \"reza01\":\"89264\", \"neda8585\":\"12379\", \"omeddd\":\"42947\"}\n",
    "blacklist = {\"neda8585\", \"omeddd\"}\n",
    "\n",
    "def ban(func):\n",
    "    @functools.wraps(func)\n",
    "    def inner(*args, **kwargs):\n",
    "        if args[0] in blacklist:\n",
    "            print(\"This user is blocked!!!\")\n",
    "        else:\n",
    "            func(*args, **kwargs)\n",
    "    return inner\n",
    "\n",
    "@ban\n",
    "def print_pass(username):\n",
    "    print(username, \":\", passwords[username])\n",
    "\n",
    "@ban\n",
    "def change_pass(username, new_pass):\n",
    "    passwords[username] = new_pass\n",
    "    print(username, \":\", passwords[username])\n",
    "\n",
    "print_pass(\"ali\")\n",
    "\n",
    "change_pass(\"neda8585\", \"33333\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ccb8eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run time of A is : 0.04921419999999799\n",
      "run time of B is : 2.3999999999801958e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# مثال از دکوراتور\n",
    "# محاسبه زمان اجرای یک تابع\n",
    "\n",
    "from time import perf_counter\n",
    "from functools import wraps\n",
    "\n",
    "def time_calc(func):\n",
    "    def wrapper_decorator(*args, **kwargs):\n",
    "        start_time = perf_counter()\n",
    "        value = func(*args, **kwargs)\n",
    "        end_time = perf_counter()\n",
    "        print(\"run time of\", func.__name__, \"is :\", end_time - start_time)\n",
    "        return value\n",
    "    return wrapper_decorator\n",
    "\n",
    "@time_calc\n",
    "def A(a):\n",
    "    s = 0\n",
    "    for i in range(a):\n",
    "        s += i**2\n",
    "    return s\n",
    "\n",
    "@time_calc\n",
    "def B(b):\n",
    "    fact = 1\n",
    "    for i in range(1, b+1):\n",
    "        fact *= i\n",
    "    return fact\n",
    "\n",
    "A(100000)\n",
    "\n",
    "B(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12738bea",
   "metadata": {},
   "source": [
    "# Generator ژنراتور"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "130b5ba6",
   "metadata": {},
   "source": [
    "ژنراتورها توابعی هستند که میتوان در حین اجرا آنها را متوقف کرد و سپس ادامه داد. این توابع شیءای را برمیگردانند که میتوان روی آنها پیمایش کرد. بر خلاف لیستها، ژنراتورها لیزی (کندرو) هستند که موجب میشود که محاسبه و ارزیابی یک عبارت تا جای ممکن به تاخیر افتاده و در اولین زمانی که دسترسی به آن نیاز بود، محاسبه شود. این توابع هنگام کار با مجموعه داده های بزرگ استفاده از حافظه را کارآمدتر میکنند."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a92b84e",
   "metadata": {},
   "source": [
    "برای فراخوانی مقادیر بعدی در ژنراتور از متد\n",
    "نکست\n",
    "استفاده می کنیم.\n",
    "در توابع معمولی برای برگرداندن مقادیر از ریترن استفاده میکنیم ولی در ژنراتور از ییلد (دستور ییلد به این صورت عمل میکند که تابع را متوقف و وضعیت آن را ذخیره میکند تا در آینده از همانجایی که متوقف شده بود به کار خود ادامه دهد.)\n",
    "ژنراتورها همچنین ایتریتور هم هستند."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8159062f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object my_generator at 0x7f7212f035a0>\n",
      "reza\n",
      "25\n",
      "\n",
      "hello\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# سینتکس\n",
    "def my_generator(args):\n",
    "    print(\"reza\")\n",
    "    yield args**2\n",
    "    print(\"hello\")\n",
    "    yield 5\n",
    "    \n",
    "g = my_generator(5)\n",
    "print(g)\n",
    "print(next(g))\n",
    "print()\n",
    "print(next(g))\n",
    "\n",
    "# وقتی به ییلد رسید وایمیسته و بعد از دوباره نکست تابع بقیه کد ها را اجرا میکند\n",
    "# مای جنراتور یک آبجکت از نوع ژنراتور است برای استفاده لازم به یک متغیر اختصاص داده شود"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef94e3e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# ژنراتور فقط وقتی نیاز باشد اجرا می شود و لذا می توان از آن برای صرفه جویی در منابع سیستمی استفاده کرد\n",
    "# مثل رنج\n",
    "\n",
    "def func():\n",
    "    for i in range(1000):\n",
    "        yield i**2\n",
    "        \n",
    "x = func()\n",
    "print(next(x))\n",
    "print(next(x))\n",
    "print(next(x))\n",
    "print(next(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4460f7f6-d17b-48f8-a3a0-23d5e200d956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def gen_range(start, end, step = 1):\n",
    "    while start < end:\n",
    "        yield start\n",
    "        start += step\n",
    "# براینکه خطای ایتریشن را مدیریت کنیم همانند ایتریتور ها تابع را درون حلقه فوور قرار می دهیم\n",
    "for i in gen_range(1, 5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28ad0fab-3a3a-4ac5-b9cf-9850ba8be94d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln: 0.08669136299795355\n",
      "gen: 0.07387538400143967\n"
     ]
    }
   ],
   "source": [
    "# در ژنراتور نیاز به اسخت همه اعداد نیست و هرموقع که بخاهیم تولید می شوند\n",
    "# برای فانکشن ساده لیست ابتدا باید ایجاد شود اما در ژنراتور نیاز به ساختن همه تابع نیست\n",
    "from time import perf_counter\n",
    "\n",
    "def list_range(start, end, step = 1):\n",
    "    new_range = []\n",
    "    while start < end:\n",
    "        new_range.append(start)\n",
    "        start += step\n",
    "    return new_range\n",
    "        \n",
    "        \n",
    "def gen_range(start, end, step = 1):\n",
    "    while start < end:\n",
    "        yield start\n",
    "        start += step\n",
    "        \n",
    "        \n",
    "# ----------------------\n",
    "start = perf_counter()\n",
    "lr = list_range(1 , 100000)\n",
    "s = 0\n",
    "for i in list_range(1 , 1000000):\n",
    "    if i == 3:\n",
    "        break\n",
    "    s += i**2\n",
    "end = perf_counter()\n",
    "print(\"ln:\" , end-start)\n",
    "\n",
    "# ----------------------\n",
    "start2 = perf_counter()\n",
    "gr = gen_range(1 , 100000)\n",
    "s = 0\n",
    "for j in list_range(1 , 1000000):\n",
    "    if j == 3:\n",
    "        break\n",
    "    s += i**2\n",
    "end2 = perf_counter()\n",
    "print(\"gen:\" , end2-start2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25e0e100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n",
      "[]\n",
      "285\n",
      "0\n",
      "******************************\n",
      "0\n",
      "1\n",
      "4\n",
      "9\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error!!!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 32\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m g:\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m16\u001b[39m:\n\u001b[0;32m---> 32\u001b[0m         \u001b[43mg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;167;43;01mValueError\u001b[39;49;00m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mError!!!\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;28mprint\u001b[39m(i)\n",
      "Cell \u001b[0;32mIn[8], line 8\u001b[0m, in \u001b[0;36mmy_generator\u001b[0;34m(r)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmy_generator\u001b[39m(r \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m):\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(r):\n\u001b[0;32m----> 8\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m i\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n",
      "\u001b[0;31mValueError\u001b[0m: Error!!!"
     ]
    }
   ],
   "source": [
    "# ژنراتور 3 متد مهم دارد\n",
    "# close برای بستن ژنراتور\n",
    "# throw برای ارائه اعمال در استثنا ها و خطا ها\n",
    "# send\n",
    "\n",
    "def my_generator(r = 10):\n",
    "    for i in range(r):\n",
    "        yield i**2\n",
    "\n",
    "j = my_generator()\n",
    "print(list(j))\n",
    "print(list(j))\n",
    "\n",
    "y = my_generator()\n",
    "print(sum(y))\n",
    "\n",
    "t = my_generator()\n",
    "print(min(t))\n",
    "\n",
    "\n",
    "for i in j:\n",
    "    if i == 16:\n",
    "        j.close()\n",
    "    print(i)\n",
    "\n",
    "print(\"*\"*30)\n",
    "    \n",
    "g = my_generator()\n",
    "\n",
    "for i in g:\n",
    "    if i == 16:\n",
    "        g.throw(ValueError(\"Error!!!\"))\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f97a6484-d28b-4a68-b2a7-1e2d83c1fa00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1400\n"
     ]
    }
   ],
   "source": [
    "# ژنراتور های تو در تو\n",
    "def square_gn(nums):\n",
    "    for i in nums:\n",
    "        yield i**2\n",
    "        \n",
    "def even_gn(nums):\n",
    "    for i in nums:\n",
    "        if i%2 == 0:\n",
    "            yield i\n",
    "            \n",
    "sg = square_gn([1,2,3,4])\n",
    "sg = even_gn([1,2,3,4])\n",
    "\n",
    "print(sum(square_gn(even_gn([12,34,5,6,8]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c250d508-d3bb-443c-958e-78183854d632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "1\n",
      "2\n",
      "3\t5\t8\t13\t21\t34\t55\t89\t144\t233\t"
     ]
    }
   ],
   "source": [
    "def fibunachi ():\n",
    "    f1 = 0\n",
    "    yield f1\n",
    "    f2 = 1\n",
    "    yield f2\n",
    "    while True:\n",
    "        f3 = f1+f2\n",
    "        yield f3\n",
    "        f1 = f2\n",
    "        f2 = f3\n",
    "        \n",
    "f = fibunachi()\n",
    "print(next(f))\n",
    "print(next(f))\n",
    "print(next(f))\n",
    "print(next(f))\n",
    "\n",
    "for _ in range(10):\n",
    "    print(next(f) , end=\"\\t\")\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4dbb077b-baf7-4081-bac3-5ace33af9516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i\n",
      "n\n",
      "i\n",
      "m\n",
      "a\n",
      " \n",
      "i\n",
      "l\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "def revers_str (s):\n",
    "    l = len(s)\n",
    "    for i in range(l-1 , -1 , -1):\n",
    "        yield s[i]\n",
    "        \n",
    "for i in revers_str(\"ali amini\"):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c19a7a7d-5253-4832-bd34-d62117305675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\t\n",
      "2\t2\t\n",
      "3\t3\t3\t\n",
      "4\t4\t4\t4\t\n",
      "5\t5\t5\t5\t5\t\n",
      "6\t6\t6\t6\t6\t6\t\n",
      "7\t7\t7\t7\t7\t7\t7\t\n",
      "8\t8\t8\t8\t8\t8\t8\t8\t\n",
      "9\t9\t9\t9\t9\t9\t9\t9\t9\t\n",
      "10\t10\t10\t10\t10\t10\t10\t10\t10\t10\t\n"
     ]
    }
   ],
   "source": [
    "def num_gen ():\n",
    "    c = 1\n",
    "    while True:\n",
    "        s = \"\"\n",
    "        for i in range(1 , c+1):\n",
    "            s += f\"{c}\\t\"\n",
    "        yield s\n",
    "        c += 1\n",
    "        \n",
    "g = num_gen() \n",
    "\n",
    "for i in range(10):\n",
    "    print(next(g))\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6339ccc5",
   "metadata": {},
   "source": [
    "# Coroutine رفتار کروتین در تابع"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41305e2c",
   "metadata": {},
   "source": [
    "کروتین ها گونه ای از توابع هستند که همزمانی را از طریق چندوظیفگی مشارکتی امکان پذیر میکنند. (cooperative multitasking) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3927fe",
   "metadata": {},
   "source": [
    "در عمل در ییلد تابع متوقف شده و با دریافت ورودی تا ییلد بعدی ادامه پیدا میکند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "068c7916-e36a-42ff-915d-07edbf4ae0c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start!\n",
      "name:  None\n",
      "name:  None\n"
     ]
    }
   ],
   "source": [
    "def my_gen():\n",
    "    print(\"Start!\")\n",
    "    while True:\n",
    "        name = yield 1\n",
    "        print(\"name: \" , name)\n",
    "            \n",
    "    \n",
    "gn = my_gen()\n",
    "next(gn)\n",
    "next(gn)\n",
    "next(gn)\n",
    "\n",
    "# مقدار ییلد ب صورت دیفالت  none  را برمیگرداند\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4ff458-d1fa-4cda-893c-ba3b8ca2b082",
   "metadata": {},
   "source": [
    "میتونیم بگیم ییلد چه چیزی برگرداند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e741b038-95c4-4a7d-a082-cfd68dd1cee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start!\n",
      "1\n",
      "name:  reza\n",
      "1\n",
      "name:  None\n",
      "1\n",
      "name:  amir\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# باید از سند استفاده بکنیم\n",
    "# باید قبل سند نکست را صدا بزنیم \n",
    "\n",
    "def gen():\n",
    "    print(\"Start!\")\n",
    "    while True:\n",
    "        name = yield 1\n",
    "        print(\"name: \" , name)\n",
    "            \n",
    "    \n",
    "g = gen()\n",
    "\n",
    "print(next(g))\n",
    "print(g.send(\"reza\"))\n",
    "# print(g.__next__)\n",
    "print(next(g))\n",
    "print(g.send(\"amir\"))\n",
    "\n",
    "\n",
    "# همزمان سند ییلد هم انجام میشود\n",
    "# اول ی چیزی ییلد میشه و بعد سند انجام میشه"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69cd7777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start!\n",
      "name:  reza\n",
      "1\n",
      "name:  amir\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# حتما لازم هست برای بار اول نکست استفاده شود\n",
    "# براینکه از نکست استفاده نکنیم میتونیم از دکرتور ها ساتفاده بکنیم\n",
    "# گام اول یک چیزی ییلد می شود و درگام بعد مقدار گرفته و پیش می رود\n",
    "from functools import wraps\n",
    "\n",
    "def coroutin(func):\n",
    "    @wraps(func)\n",
    "    def start (*a , **kw):\n",
    "        gn = func()\n",
    "        next(gn)\n",
    "        return gn\n",
    "    return start\n",
    "\n",
    "@coroutin\n",
    "\n",
    "def gen():\n",
    "    print(\"Start!\")\n",
    "    while True:\n",
    "        name = yield 1\n",
    "        print(\"name: \" , name)\n",
    "            \n",
    "    \n",
    "g = gen()\n",
    "\n",
    "print(g.send(\"reza\"))\n",
    "print(g.send(\"amir\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf63a747",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start!\n",
      "reza\n",
      "****\n",
      "ali\n",
      "***\n"
     ]
    }
   ],
   "source": [
    "# مثال\n",
    "# سانسور\n",
    "\n",
    "def cen_gen(words):\n",
    "    print(\"Start!\")\n",
    "    w = None\n",
    "    while True:\n",
    "        word = yield w\n",
    "        if word not in words:\n",
    "            w = word\n",
    "        else:\n",
    "            w = \"*\" * len(word)\n",
    "\n",
    "gn = cen_gen([\"khar\", \"gav\", \"meymoon\"])\n",
    "next(gn)\n",
    "print(gn.send(\"reza\"))\n",
    "print(gn.send(\"khar\"))\n",
    "print(gn.send(\"ali\"))\n",
    "print(gn.send(\"gav\"))\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eedb90df",
   "metadata": {},
   "source": [
    "# صفات تابع attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a13669",
   "metadata": {},
   "source": [
    "در پایتون همه چی شیء هستند و صفات خود را دارند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1fc0da08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']\n"
     ]
    }
   ],
   "source": [
    "x = 2\n",
    "print(dir(x))  # برای فراخوانی صفات شیء"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0d580ece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__annotations__', '__builtins__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'school_name']\n",
      "tabatabayi\n",
      "bahonar\n"
     ]
    }
   ],
   "source": [
    "# می توان برای تابع صفت دلخواه اضافه کرد\n",
    "\n",
    "def ave(li):\n",
    "    return sum(li)/len(li)\n",
    "\n",
    "def ave2(li):\n",
    "    return sum(li)/len(li)\n",
    "\n",
    "\n",
    "ave.school_name = \"tabatabayi\"  # ساده ترین راه برای تعریف صفت جدید برای تابع\n",
    "ave2.school_name = \"bahonar\"\n",
    "\n",
    "print(dir(ave))   \n",
    "print(ave.school_name)\n",
    "print(ave2.school_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "207f0a54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabatabyi\n",
      "{'school_name': 'tabatabyi', 'creator': 'reza'}\n",
      "default\n"
     ]
    }
   ],
   "source": [
    "# setattr برای تعریف صفت برای آبجکت\n",
    "# setattr(object, attr_name, attr_value)\n",
    "\n",
    "def ave(li):\n",
    "    return sum(li)/len(li)\n",
    "\n",
    "setattr(ave, \"school_name\", \"tabatabyi\")\n",
    "setattr(ave, \"creator\", \"reza\")\n",
    "\n",
    "print(ave.school_name)\n",
    "print(ave.__dict__)  # دیکشنری از صفات تعریف شده\n",
    "\n",
    "print(getattr(ave, \"name\", \"default\"))  # ابزاری باری برگرداندن مقدار صفت. اگر آن صفت در شیء نباشد مقدار دیفالت را بر میگرداند\n",
    "\n",
    "if hasattr(ave, \"name\"):   # برررسی میکند که آن صفت در شیء موجود است یا نه\n",
    "    print(getattr(ave, \"name\"))\n",
    "                  \n",
    "# delattr(object, attr_name)  # برای حذف صفت\n",
    "delattr(ave, \"school_name\")\n",
    "\n",
    "# در مواقع استفاده از اتریبوت ها در دکرتور ها ار فانک توولز استفادخ باید کرد براینکه اتریبوت ها حفظ شود"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9dbf20b",
   "metadata": {},
   "source": [
    "# Recursive Func توابع بازگشتی"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94c90c4",
   "metadata": {},
   "source": [
    "هر تابعی که داخل خودش خودش را فرابخواند تابع بازگشتی است. کلمه کلیدی ندارد و اصل مساله نحوه تعریف و حل مساله است"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "3ecb9226",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9b9b6b",
   "metadata": {},
   "source": [
    "تابعی که درون خودش خودش را صدا میزند\n",
    "ساختار تابع بازگشتی دو بخش اصلی دارد: 1.شرط توقف 2.ادامه چرخه تابع "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3231a8b2",
   "metadata": {},
   "source": [
    "توابع بازگشتی حافظه بیشتری مصرف کرده و کند تر هستند. بسته به الگوریتم لازم هست بررسی شود تابع بازگشتی لازم هست یا نه."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cdccc2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n"
     ]
    }
   ],
   "source": [
    "# فاکتوریل\n",
    "# 5! = 5 * 4 * 3 * 2 * 1\n",
    "# 5! = 5 * 4!\n",
    "\n",
    "def rec_fact(x):\n",
    "    if x == 0 or x == 1:\n",
    "        return 1\n",
    "    return x * rec_fact(x-1)  # 5! = 5 * 4!\n",
    "\n",
    "print(rec_fact(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "58612f0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# جمله ان ام دنباله فیبوناچی\n",
    "# ۰، ۱، ۱، ۲، ۳، ۵، ۸، ۱۳، ۲۱، ۳۴، ۵۵، ۸۹، ۱۴۴، ۲۳۳، ۳۷۷، ۶۱۰، ۹۸۷، ۱۵۹۷، ۲۵۸۴، ۴۱۸۱، ۶۷۶۵، ۱۰۹۴۶، ۱۷۷۱۱\n",
    "\n",
    "def rec_fib(n):\n",
    "    if n == 1:\n",
    "        return 0\n",
    "    elif n == 2:\n",
    "        return 1\n",
    "    else:\n",
    "        return rec_fib(n-1) + rec_fib(n-2)\n",
    "\n",
    "print(rec_fib(5))\n",
    "\n",
    "# تمام جملات دنباله در تابع بازگشتی حساب می شوند ولی جمله مورد نظر به عنوان نهایی برگردانده می شود\n",
    "# تابع بازگشتی ساختار پشته یا استک دارد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5f3b989f-fdbd-4665-9fbc-13b006095c3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# جمله ان ام دنباله فیبوناچی\n",
    "# ۰، ۱، ۱، ۲، ۳، ۵، ۸، ۱۳، ۲۱، ۳۴، ۵۵، ۸۹، ۱۴۴، ۲۳۳، ۳۷۷، ۶۱۰، ۹۸۷، ۱۵۹۷، ۲۵۸۴، ۴۱۸۱، ۶۷۶۵، ۱۰۹۴۶، ۱۷۷۱۱\n",
    "\n",
    "def rec_fib(n):\n",
    "    if n == 0 or n == 1:\n",
    "        return n \n",
    "    return rec_fib(n-1) + rec_fib(n-2)\n",
    "\n",
    "print(rec_fib(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dd4b3c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | "
     ]
    }
   ],
   "source": [
    "# مثال شمارش معکوس\n",
    "from time import sleep  # به ثانیه باعث وقفه می شود\n",
    "\n",
    "def reverse(n):\n",
    "    if n <= 0:\n",
    "        return\n",
    "    sleep(0.5)\n",
    "    print(n, end=\" | \")\n",
    "    n -= 1\n",
    "    reverse(n)\n",
    "    \n",
    "reverse(10)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "708d29db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86400\n"
     ]
    }
   ],
   "source": [
    "# مثال ضرب ارقام بزرگتر از ۵ یک عدد بزرگ\n",
    "\n",
    "def mul5(n):\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    elif n % 10 < 5:\n",
    "        return mul5(n // 10)\n",
    "    else:\n",
    "        return n%10 * mul5(n//10)\n",
    "\n",
    "\n",
    "\n",
    "print(mul5(56342313218985))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37e74b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 | 27 | 24 | 21 | 18 | 15 | 12 | 9 | 6 | 3 | "
     ]
    }
   ],
   "source": [
    "# مثال چاپ مضارب 3 کوچکتر از یک عدد\n",
    "\n",
    "def func(n):\n",
    "    if n < 3:\n",
    "        return\n",
    "    elif n % 3 ==0:\n",
    "        print(n, end=\" | \")\n",
    "    func(n - 1)\n",
    "    \n",
    "func(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8befb760",
   "metadata": {},
   "outputs": [],
   "source": [
    "# استفاده از دکوراتور در تابع بازگشتی، در تمام فراخوانی های آن اجرای میشود\n",
    "# استفاده از ژنراتور در تابع های بازگشتی موارد خاص خود را دارد و استفاده از ژنراتور در تابع بازگشتی رایج نیست"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "91107e46-64b9-4ceb-afb8-de4dae58cc7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      "3\n",
      "******************************\n",
      "2\n",
      "******************************\n",
      "1\n",
      "******************************\n",
      "------------------------------\n",
      "------------------------------\n",
      "------------------------------\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "from functools import wraps\n",
    "from time import sleep\n",
    "\n",
    "\n",
    "def dec (func): \n",
    "    @wraps(func)\n",
    "    def w_f (*arg , **keyW):\n",
    "        print(\"*\" * 30)\n",
    "        value = func(*arg , **keyW)\n",
    "        print(\"-\" * 30)\n",
    "        return value\n",
    "    return w_f\n",
    "\n",
    "@dec\n",
    "def rev(n):\n",
    "    if n <= 0:\n",
    "        return\n",
    "    sleep(0.5)\n",
    "    print(n)\n",
    "    rev(n-1)\n",
    "    \n",
    "rev(3)\n",
    "# قبل اینکه تابع تموم بشه و خط تیره چاپ بشه دوباره تابع خود را فرامیخاند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0ad56f5f-29d2-43da-a5ed-e5c0f73a9519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10]\n",
      "\n",
      "[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "def g_rev2(n):\n",
    "    if n <= 0:\n",
    "        return\n",
    "    sleep(0.5)\n",
    "    yield n\n",
    "    g_rev2(n-1)\n",
    "\n",
    "        \n",
    "g2 = g_rev2(10)\n",
    "print(list(g2))\n",
    "\n",
    "print()\n",
    "\n",
    "def g_rev(n):\n",
    "    if n <= 0:\n",
    "        return\n",
    "    sleep(0.5)\n",
    "    yield n\n",
    "    for i in g_rev(n-1):\n",
    "        yield i\n",
    "        \n",
    "g = g_rev(10)\n",
    "print(list(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4eca4522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "بیشتر از این مقدار حافظه جا ندارد 1000\n"
     ]
    },
    {
     "ename": "RecursionError",
     "evalue": "cannot set the recursion limit to 10 at the recursion depth 28: the limit is too low",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[66], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mبیشتر از این مقدار حافظه جا ندارد\u001b[39m\u001b[38;5;124m\"\u001b[39m, sys\u001b[38;5;241m.\u001b[39mgetrecursionlimit())\n\u001b[0;32m----> 7\u001b[0m \u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetrecursionlimit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# تعیین اندازه حافظه ذخیره در پشته\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrev\u001b[39m(n):\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[0;31mRecursionError\u001b[0m: cannot set the recursion limit to 10 at the recursion depth 28: the limit is too low"
     ]
    }
   ],
   "source": [
    "# بررسی و محدودیت در استفاده از حافظه در تابع بازگشتی\n",
    "# فعالیت تابع بازگشتی باعث اشغال فضای ذخیره سازی می شود و می توان محدودیت را دید و یا تعیین کرد\n",
    "\n",
    "import sys\n",
    "\n",
    "print(\"بیشتر از این مقدار حافظه جا ندارد\", sys.getrecursionlimit())\n",
    "sys.setrecursionlimit(10)  # تعیین اندازه حافظه ذخیره در پشته\n",
    "\n",
    "def rev(n):\n",
    "    if n <= 0:\n",
    "        return\n",
    "    sleep(0.5)\n",
    "    print(n)\n",
    "    rev(n-1)\n",
    "    \n",
    "rev(30)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "1e898e47",
   "metadata": {},
   "source": [
    "در تابع بازگشتی ممکن است برخی برگشت های تابع چند بار حساب شوند\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c8062c",
   "metadata": {},
   "source": [
    "برای حل این مسئله از memorization استفاده میکنیم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2c7433c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63245986\n",
      "time of calculation 29.579541300000002\n"
     ]
    }
   ],
   "source": [
    "from time import perf_counter\n",
    "\n",
    "def rec_fib(n):\n",
    "    if n == 1:\n",
    "        return 0\n",
    "    elif n == 2:\n",
    "        return 1\n",
    "    else:\n",
    "        return rec_fib(n-1) + rec_fib(n-2)\n",
    "\n",
    "\n",
    "start_time = perf_counter()\n",
    "print(rec_fib(40))\n",
    "end_time = perf_counter()\n",
    "print(\"time of calculation\", end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "41e54724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63245986\n",
      "time of calculation 0.0002016300022660289\n"
     ]
    }
   ],
   "source": [
    "# دیتا هایی که محاسبه شده اند در داخل دیکشنری ذخیره میکنیم\n",
    "# استفاده از دکوراتور برای ممورایزیشن\n",
    "from time import perf_counter\n",
    "\n",
    "def memorize(func):\n",
    "    memory = {}\n",
    "    def wrapper_decoration(n):\n",
    "        if n not in memory:\n",
    "            memory[n] = func(n)\n",
    "        return memory[n]\n",
    "    return wrapper_decoration\n",
    "\n",
    "@memorize\n",
    "def rec_fib(n):\n",
    "    if n == 1:\n",
    "        return 0\n",
    "    elif n == 2:\n",
    "        return 1\n",
    "    else:\n",
    "        return rec_fib(n-1) + rec_fib(n-2)\n",
    "\n",
    "\n",
    "start_time = perf_counter()\n",
    "print(rec_fib(40))\n",
    "end_time = perf_counter()\n",
    "print(\"time of calculation\", end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c4180e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}