{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a comment, ignored by Python Script\n",
    "1 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "5 - 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "3 * 5 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 +2 = 3\n",
      "5 - 3 = 2\n",
      "3 * 5 - 2 = 13\n",
      "7 / 5 = 1.4\n",
      "2 ** 10 = 1024\n",
      "7 // 5 = 1\n",
      "7 % 5 = 2\n"
     ]
    }
   ],
   "source": [
    "# this is a comment, ignored by Python Script\n",
    "print('1 +2 =', 1 + 2)\n",
    "\n",
    "print('5 - 3 =', 5 - 3)\n",
    "\n",
    "print('3 * 5 - 2 =', 3 * 5 - 2)\n",
    "\n",
    "print('7 / 5 =', 7 / 5)\n",
    "\n",
    "print('2 ** 10 =',2 ** 10) # exponential\n",
    "\n",
    "print('7 // 5 =',7 // 5 ) # quotient\n",
    "\n",
    "print('7 % 5 =', 7 % 5 )# remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 +2 = 3 \n",
      "\n",
      "5 - 3 = 2 \n",
      "\n",
      "3 * 5 - 2 = 13 \n",
      "\n",
      "7 / 5 = 1.4 \n",
      "\n",
      "2 ** 10 = 1024 \n",
      "\n",
      "7 // 5 = 1 \n",
      "\n",
      "7 % 5 = 2 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# this is a comment, ignored by Python Script\n",
    "print('1 +2 =', 1 + 2, '\\n')\n",
    "\n",
    "print('5 - 3 =', 5 - 3, '\\n')\n",
    "\n",
    "print('3 * 5 - 2 =', 3 * 5 - 2, '\\n')\n",
    "\n",
    "print('7 / 5 =', 7 / 5, '\\n')\n",
    "\n",
    "print('2 ** 10 =',2 ** 10, '\\n') # exponential\n",
    "\n",
    "print('7 // 5 =',7 // 5, '\\n') # quotient\n",
    "\n",
    "print('7 % 5 =', 7 % 5, '\\n' )# remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# they are optional here but better\n",
    "(3 * 5) - 2\n",
    "\n",
    "# subtraction before multiplication\n",
    "3 * (5 - 2)\n",
    "\n",
    "# the order of operation is not clear\n",
    "2 ** 3 ** 2\n",
    "\n",
    "# no ambiguity, pay attention to code format\n",
    "(2 ** 3) ** 2\n",
    "\n",
    "# no ambiguity\n",
    "2 ** (3 ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4142135623730951\n",
      "0.6931471805599453\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "print(math.sqrt(2))\n",
    "\n",
    "print(math.log(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import math\n",
    "3 > 2\n",
    "3 == 2\n",
    "3 != 2\n",
    "(7 / 5) >= 1\n",
    "math.sqrt(3) > 2\n",
    "\n",
    "# can be chained\n",
    "2 > math.sqrt(3) > math.sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import math\n",
    "print('3 > 2 is ',3 > 2)\n",
    "print('3 == 2 is ', 3 == 2)\n",
    "print('3 != 2 is ', 3 != 2)\n",
    "print('(7 / 5) >= 1 is ', (7 / 5) >= 1)\n",
    "print('math.sqrt(3) > 2 is ', math.sqrt(3) > 2)\n",
    "\n",
    "# can be chained\n",
    "print('2 > math.sqrt(3) > math.sqrt(2) is ', 2 > math.sqrt(3) > math.sqrt(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "not (3 == 2)\n",
    "\n",
    "not (3 != 2)\n",
    "\n",
    "(3 > 2) and ((7 / 5) > 3)\n",
    "\n",
    "(3 == 2) or (3 >= 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('not (3 == 2) is ', not (3 == 2))\n",
    "\n",
    "print('not (3 != 2) is ', not (3 != 2))\n",
    "\n",
    "print('(3 > 2) and ((7 / 5) > 3) is ', (3 > 2) and ((7 / 5) > 3))\n",
    "\n",
    "print('(3 == 2) or (3 >= 2) is ', (3 == 2) or (3 >= 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.800000000000004\n"
     ]
    }
   ],
   "source": [
    "pi = 3.14\n",
    "\n",
    "# display a message and take user input\n",
    "radius_input = input(\"Please input radius: \")\n",
    "\n",
    "# convert input string into a float number\n",
    "radius = float(radius_input)\n",
    "\n",
    "diameter = 2 * radius\n",
    "circumference = pi * diameter\n",
    "\n",
    "print(circumference)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The circumference of radius 10.00 is 62.831853.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# display a message and take user input\n",
    "radius_input = input(\"Please input radius: \")\n",
    "# convert input text into a float number\n",
    "radius = float(radius_input)\n",
    "diameter = 2 * radius\n",
    "\n",
    "# Python has a predefined pi variable in the math module\n",
    "circumference = math.pi * diameter\n",
    "print(f\"The circumference of radius {radius:.2f} is {circumference:.6f}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "your choices = ['Nuclear Engineering', 'Electronics', 'Chemistry']\n"
     ]
    }
   ],
   "source": [
    "# display a message and take user input\n",
    "score_input = input(\"Please input your score: \")\n",
    "# convert input text into an integer\n",
    "score = float(score_input)\n",
    "if score >= 16: #DZD\n",
    "    choices = [\"Medical Sciences\", \"Dentist\", \"Computer Science\", \"High School\"]\n",
    "elif 12 <= score < 16:\n",
    "    choices = [\"Nuclear Engineering\", \"Electronics\", \"Chemistry\"]\n",
    "else:\n",
    "    choices = [\"Mechanical Engineering\", \"Electrical Engineering\"]\n",
    "# back to normal\n",
    "print('your choices =', choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please keep studying and practicing....\n",
      "Please keep studying and practicing....\n",
      "Great, you made it!\n"
     ]
    }
   ],
   "source": [
    "score_input = input(\"Please input your score: \")\n",
    "score = float(score_input)\n",
    "\n",
    "while score < 14:\n",
    "    print(\"Please keep studying and practicing....\")\n",
    "    score_input = input(\"Please input new score: \")\n",
    "    score = float(score_input)\n",
    "\n",
    "# back to normal \n",
    "print(\"Great, you made it!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "B\n",
      "C\n"
     ]
    }
   ],
   "source": [
    "scores = [12, 13.5, 18, 10.5, 15.]\n",
    "grades = [\"C\", \"C\", \"A\", \"D\", \"B\"]\n",
    "\n",
    "print(scores[0])\n",
    "print(grades[4])\n",
    "print(grades[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You need 6 points to get an A.\n",
      "You need 4.5 points to get an A.\n",
      "Great, you got an A.\n",
      "You need 7.5 points to get an A.\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "scores = [12, 13.5, 18, 10.5]\n",
    "# in each iteration of this for loop\n",
    "# the score is assigned the next value in the list\n",
    "# starting from index 0 to the last one.\n",
    "for score in scores:\n",
    "    if score >= 18:\n",
    "        print(f\"Great, you got an A.\")\n",
    "    else:\n",
    "        difference = 18 - score\n",
    "        print(f\"You need {difference} points to get an A.\")\n",
    "\n",
    "print(\"Done.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "electron\n",
      "fundamental particle\n"
     ]
    }
   ],
   "source": [
    "# a dictionary has a comma-separated list of key:value pairs within the braces\n",
    "Particle01 = {'Name':'electron', 'class':'fundamental particle', 'Discovery':1897, 'by':'J.J.Thomson',\n",
    "            'Mass':9.109e-31, 'Charge':1.602e-19}\n",
    "\n",
    "# use a key to read or change a dictionary value\n",
    "print(Particle01[\"Name\"])\n",
    "print(Particle01[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Particle01[\"spin\"] = '1/2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Name': 'electron', 'class': 'fundamental particle', 'Discovery': 1897, 'by': 'J.J.Thomson', 'Mass': 9.109e-31, 'Charge': 1.602e-19, 'spin': '1/2'}\n"
     ]
    }
   ],
   "source": [
    "print(Particle01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key Name has a value of electron\n",
      "Key class has a value of fundamental particle\n",
      "Key Discovery has a value of 1897\n",
      "Key by has a value of J.J.Thomson\n",
      "Key Mass has a value of 9.109e-31\n",
      "Key Charge has a value of 1.602e-19\n",
      "Key spin has a value of 1/2\n"
     ]
    }
   ],
   "source": [
    "# access every key and its value\n",
    "for key in Particle01.keys():\n",
    "    message = f\"Key {key} has a value of {Particle01[key]}\"\n",
    "    print(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_chatgpt(dico):\n",
    "    print(f\"\"\"\n",
    "    The {dico['Name']} is a {dico['class']}, discovered in {dico['Discovery']} by {dico['by']}.\n",
    "    It has an electrical charge q = {dico['Charge']}, a mass m={dico['Mass']} and a spin s = {dico['spin']}.\n",
    "\"\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    The electron is a fundamental particle, discovered in 1897 by J.J.Thomson.\n",
      "    It has an electrical charge q = 1.602e-19, a mass m=9.109e-31 and a spin s = 1/2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "my_chatgpt(Particle01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "# define a function with a parameter radius\n",
    "# the body is a code block to do the real work\n",
    "def get_circumference(radius):\n",
    "    diameter = 2 * radius\n",
    "    circumference = math.pi * diameter\n",
    "    return circumference\n",
    "\n",
    "radiuses = [1, 5, 10]\n",
    "for radius in radiuses:\n",
    "    # here the radius is the argument of the function call\n",
    "    circumference = get_circumference(radius)\n",
    "    print(f\"The circumference is {circumference:.4f}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_circumference(120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(\"hi\") # <class 'str'>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(3) # <class 'int'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"hi\".title())\n",
    "\n",
    "import math\n",
    "\n",
    "print(math.pi) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High.\n",
      "Low.\n",
      "Low.\n",
      "Low.\n",
      "High.\n",
      "Low.\n",
      "Low.\n",
      "Low.\n",
      "Low.\n",
      "High.\n",
      "Bingo.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "# random.seed(3024)\n",
    "the_number = random.randint(0, 100)\n",
    "\n",
    "while True:\n",
    "    user_input = input('Please guess the number between 0 and 100: ')\n",
    "    try:\n",
    "        user_number = int(user_input)\n",
    "        if user_number == the_number:\n",
    "            print('Bingo.')\n",
    "            break\n",
    "        elif user_number > the_number:\n",
    "            print('High.')\n",
    "        else:\n",
    "            print('Low.')\n",
    "    except ValueError:\n",
    "        print('Invalid input, please try again')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'random' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mrandom\u001b[49m\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m100\u001b[39m))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'random' is not defined"
     ]
    }
   ],
   "source": [
    "print(random.randint(0, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('My Inline Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x =  np.linspace(0,10,10)\n",
    "f = lambda x: np.cos(x)\n",
    "plt.plot(x, f(x))\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('cosine function')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x =  np.linspace(0,10,100)\n",
    "f = lambda x: np.cos(x)\n",
    "plt.plot(x, f(x), c='r', ls='-', lw='1.75')\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('cosine function')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOoBwCdjcYmI+aemRUS4nrX",
   "provenance": [
    {
     "file_id": "1D5v5zez1kZbhm8h-pkf34ntmjB-VDZYo",
     "timestamp": 1697017004970
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}