{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48e18873",
   "metadata": {},
   "source": [
    "# M0: Introduction to Python\n",
    "\n",
    "# Build your conda environment\n",
    "\n",
    "Before you run any code, you should install all the necessary packages in a conda environment\n",
    "\n",
    "* First step: Install miniconda https://docs.conda.io/en/latest/miniconda.html\n",
    "* Open a conda terminal and create the conda environment that we are going to call p4env.\n",
    "\n",
    "        conda create --name pchem\n",
    "        conda activate pchem\n",
    "\n",
    "\n",
    "Necessary packages: \n",
    "\n",
    "Use the terminal application for the commands below. Everytime you open the terminal the first step is to activate your environment pchem where you are installing all your software by typing: \"conda activate pchem\"\n",
    "\n",
    "    conda install python=3.12\n",
    "    pip install pyscf\n",
    "    pip install geometric\n",
    "    conda install -c conda-forge matplotlib\n",
    "    conda install -c conda-forge notebook\n",
    "\n",
    "Additional packages\n",
    "\n",
    "    conda install sympy\n",
    "\n",
    "    conda install -c conda-forge ipywidgets\n",
    "    conda install -c plotly plotly\n",
    "\n",
    "    conda install pandas\n",
    "    conda install -c conda-forge py3dmol\n",
    "\n",
    "The prefered way to edit and interact with all the class material is by using Visual Studio Code (https://code.visualstudio.com/). It is free of charge but propietary.\n",
    "Alternatively, anyone can also use the jupyter-notebook installed above. In this latter case, in the terminal, type jupyter-notebook (in Mac) or jupyter notebook (in Windows). A new tab in your browser should appear. Learn to navigate through files using jupyter notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbff4e5",
   "metadata": {},
   "source": [
    "# Types of variables 1: integers, floats, and strings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098d2ac0",
   "metadata": {},
   "source": [
    "## Integers, floats, and strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b906284b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the variable \"a\" is an integer\n",
    "a=23\n",
    "#the variable \"b\" is a float\n",
    "b=23.0\n",
    "#the variable \"c\" is a string\n",
    "c=\"23\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af27e9e6",
   "metadata": {},
   "source": [
    "## Operations with integers, floats, and strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6311ef30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.0\n",
      "23.0\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "#the operation between integers and floats give floats\n",
    "print(a+b)\n",
    "#one can convert integers into floats and viceversa\n",
    "print( float(a) )\n",
    "print( int(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936c627c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#typical operations are +, -, *, /\n",
    "print(a*b)\n",
    "# for more sophisticated operations we will use the \"math\" module or \"numpy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "365351be",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#strings and numbers (int or floats)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mc\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43ma\u001b[49m)\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "#strings and numbers (int or floats)\n",
    "print(c+a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70c83deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.0\n"
     ]
    }
   ],
   "source": [
    "#but if a string has numbers it can be converted into a number\n",
    "print(float(c)+a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05dbe29d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **   variable\n",
      " **  this is a\n",
      "this is a variable\n",
      "this is a string variable\n"
     ]
    }
   ],
   "source": [
    "#Two strings can be joined or concatenated with +\n",
    "e='this is a'\n",
    "f=' variable'\n",
    "print(' ** ', f)\n",
    "print(' ** ',e)\n",
    "print(e+f)\n",
    "print(e+' string'+f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4394c8fe",
   "metadata": {},
   "source": [
    "## Using modules or libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2cdb1c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.8462204041751706\n",
      "9744803446.248903\n",
      "3.141592653589793\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "print(math.sin(a))\n",
    "print(math.exp(a))\n",
    "print(math.pi / math.exp(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd75bcf",
   "metadata": {},
   "source": [
    "# Types of variables 2: lists and dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "93742a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-23 7\n",
      "['c', 'd']\n"
     ]
    }
   ],
   "source": [
    "#a list is indicated with the square bracket. A list can contain integers, floats, strings or more lists.\n",
    "\n",
    "#a list of integers\n",
    "g = [-23,6,-34,7]\n",
    "\n",
    "#access the elements of the list with brackets starting to count at zero\n",
    "print(g[0],g[3])\n",
    "\n",
    "#you can also print a range using ':'\n",
    "h=['a','b','c','d','e']\n",
    "print(h[2:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "27e6fb7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'a', 'long', 'string', 'made', 'out', 'of', 'words', 'that', 'it', 'is', 'about', 'to', 'be', 'split']\n",
      "This split be\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a slit with the function 'split'\n",
    "i='This is a long string made out of words that it is about to be split'.split()\n",
    "print(i)\n",
    "#print first and last\n",
    "print(i[0],i[-1],i[-2])\n",
    "\n",
    "#using len you can see how many elements the list has\n",
    "print( len(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7fa57f0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is number one\n",
      "this is a string\n"
     ]
    }
   ],
   "source": [
    "#a dictionary uses curled brackets and instead of \n",
    "\n",
    "j={1:'this is number one',\n",
    "  'a':'this is the letter a',\n",
    "  '2':'this is a string'}\n",
    "\n",
    "print(j[1])\n",
    "print(j['2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc81cc8",
   "metadata": {},
   "source": [
    "# Main structures: Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7e8c4498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "#looping through a list\n",
    "\n",
    "myLoop = [2,3,4,5,6]\n",
    "for thisThing in myLoop:\n",
    "    print(thisThing*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "91967c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "#using range for a range of numbers\n",
    "for this in range(10):\n",
    "    print(this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "176d5839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n",
      "e\n",
      "f\n",
      "g\n",
      "h\n",
      "i\n",
      "j\n",
      "k\n",
      "l\n",
      "This is element c\n",
      "This is element d\n",
      "This is element e\n"
     ]
    }
   ],
   "source": [
    "#Looping through a string\n",
    "thisString = 'abcdefghijkl'\n",
    "for z in thisString:\n",
    "    print(z)\n",
    "for z in thisString[2:5]:\n",
    "    print(\"This is element\",z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe8fed5",
   "metadata": {},
   "source": [
    "# Main structures: Conditionals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b4fef09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 is smaller than 10\n",
      "40 is larger than 10\n"
     ]
    }
   ],
   "source": [
    "a=4\n",
    "if a < 10:\n",
    "    print(a,\"is smaller than 10\")\n",
    "else:\n",
    "    print(a,\"is larger than 10\")\n",
    "a=40\n",
    "if a < 10:\n",
    "    print(a,\"is smaller than 10\")\n",
    "else:\n",
    "    print(a,\"is larger than 10\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da99fde",
   "metadata": {},
   "source": [
    "## Combining loops and conditionals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b2783fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This word contains the letter r: Four\n",
      "This word contains the letter r: score\n",
      "This word contains the letter r: years\n",
      "This word contains the letter r: our\n",
      "This word contains the letter r: fathers\n",
      "This word contains the letter r: brought\n",
      "This word contains the letter r: forth\n",
      "This word contains the letter r: Liberty,\n",
      "This word contains the letter r: proposition\n",
      "This word contains the letter r: are\n",
      "This word contains the letter r: created\n"
     ]
    }
   ],
   "source": [
    "gettys='Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal'\n",
    "\n",
    "for word in gettys.split():\n",
    "    if 'r' in word:\n",
    "        print('This word contains the letter r:',word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8abc962",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8075ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115325910>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=[1,2,3,4,5]\n",
    "y=[-10,-20,30,40,-50]\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e31333dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,label='this label')\n",
    "plt.xlabel(\"Title for the x axis\")\n",
    "plt.ylabel(\"Title for the y axis\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Main title\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d627373",
   "metadata": {},
   "source": [
    "## More plotting options with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71d68d69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-20, -40, 60, 80, -100]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "\n",
    "ax1 = plt.subplot(121) #121 means: the subplots will be 1 row 2 columns and this one is subplot number 1\n",
    "ax2 = plt.subplot(122, sharey = ax1) #share-y means that it will share the y-axis with the ax1 plot\n",
    "#for 3x3 plot matrix you would have written subplot(331) 332 333 334...\n",
    "\n",
    "y2 = [n*2 for n in y]\n",
    "y3 = [n*3 for n in y]\n",
    "print(y2)\n",
    "ax1.plot(x,y2,'-o')\n",
    "ax2.plot(x,y3,'--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9680a6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "ax1 = plt.subplot(131) #121 means: the subplots will be 1 row 2 columns and this one is subplot number 1\n",
    "ax2 = plt.subplot(132, sharey = ax1) #share-y means that it will share the y-axis with the ax1 plot\n",
    "ax3 = plt.subplot(133, sharey = ax1) \n",
    "\n",
    "for n in range(10):\n",
    "    ax1.axhline(y=n*2,xmin=-2., xmax=2., c='green')\n",
    "for n in range(10):\n",
    "    ax2.axhline(y=n*0.5,xmin=0., xmax=2., c='blue')\n",
    "for n in range(10):\n",
    "    ax3.axhline(y=n*0.3,xmin=0., xmax=2., c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53792ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y = [1]*(len(y2)) # this is the height of the signal. It doesnt matter, just height = 1\n",
    "fig, ax = plt.subplots()\n",
    "ax.stem(y2, y, markerfmt=' ',linefmt=\"red\")\n",
    "ax.set_xlabel(\"Energy of the transition\")\n",
    "ax.set_ylabel(\"Absorbance\")\n",
    "ax.set_title(\"Absorption\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18d213f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.normal(170, 10, 250)\n",
    "\n",
    "plt.hist(x)\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pchem",
   "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": "3.12.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}