TOC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b44b0b54-05e5-4bc0-a39e-5858baf780b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "h1, h2, h3, h4, h5, h6 {\n",
       "  font-family: Verdana, \"DejaVu Sans\", \"Bitstream Vera Sans\", Geneva, sans-serif;\n",
       "  font-weight: bold;\n",
       "}\n",
       "body, intro, introE, introT, rq, rqE, rqT, ex, exE, app, appE, sol, todo, hint, hintE, figure  {\n",
       "  font-family: Verdana, \"DejaVu Sans\", \"Bitstream Vera Sans\", Geneva, sans-serif;\n",
       "  font-weight: 400;\n",
       "  font-size: 12px;\n",
       "}\n",
       "h1 {\n",
       "  border: 0 solid #333;\n",
       "  padding: 30px ;\n",
       "  color: white;\n",
       "  background: #b11d01;\n",
       "  text-align: center;\n",
       "}\n",
       "h2 {\n",
       "  border: 3px solid #333;\n",
       "  padding: 18px ;\n",
       "  color: #b11d01;\n",
       "  background: #ffffff;\n",
       "  text-align: center;\n",
       "}\n",
       "h3 {\n",
       "  border: 0 solid #333;\n",
       "  padding: 12px ;\n",
       "  color: #000000;\n",
       "  background: #c1c1c1;\n",
       "  text-align: left;\n",
       "}\n",
       "h4 {\n",
       "  border: 0 solid #333;\n",
       "  padding: 2px ;\n",
       "  color: #000000;\n",
       "  background: #d9fffc;\n",
       "  text-align: left;\n",
       "}\n",
       "h5 {\n",
       "  border: 1px solid #333;\n",
       "  padding: 2px ;\n",
       "  color: #000000;\n",
       "  background: #ffffff;\n",
       "  text-align: left;\n",
       "}\n",
       ".introT::before {    \n",
       "    content: attr(title);\n",
       "    background-color: #cecece;\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".introT {    \n",
       "    background-color: #cecece80;\n",
       "    border-color: #969696;\n",
       "    border-left: 5px solid #969696;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".intro {    \n",
       "    background-color: #cecece80;\n",
       "    border-color: #969696;\n",
       "    border-left: 5px solid #969696;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".introE {    \n",
       "    background-color: #cecece80;\n",
       "    border-color: #969696;\n",
       "    border-left: 5px solid #969696;\n",
       "    padding: 0.5em;\n",
       "    color : #117996;\n",
       "}\n",
       ".rq::before {    \n",
       "    background-color: #fcd3d3;\n",
       "    color: #ff0000;\n",
       "    content:\"Remarque\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".rq {    \n",
       "    background-color: #fcf2f2;\n",
       "    border-color: #dFb5b4;\n",
       "    border-left: 5px solid #dfb5b4;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".rqE::before {    \n",
       "    background-color: #fcd3d3;\n",
       "    color: #ff0000;\n",
       "    content:\"Pay attention\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".rqE {    \n",
       "    background-color: #fcd3d380;\n",
       "    border-color: #dFb5b4;\n",
       "    border-left: 5px solid #dfb5b4;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".rqT::before {    \n",
       "    background-color: #fcd3d3;\n",
       "    color: #ff0000;\n",
       "    content: attr(title);\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".rqT {    \n",
       "    background-color: #fcd3d380;\n",
       "    border-color: #dFb5b4;\n",
       "    border-left: 5px solid #dfb5b4;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".exold {    \n",
       "    background-color: #b2dbea80;\n",
       "    border-color: #0055ff;\n",
       "    border-left: 10px solid #0055ff;\n",
       "    padding: 0.5em;\n",
       "}\n",
       ".ex {    \n",
       "    background-color: #b2dbea80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "}\n",
       ".ex::before {\n",
       "    background-color: #b2dbea;\n",
       "    content:\"Exercice. \" attr(title);\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".exE {    \n",
       "    background-color: #b2dbea80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "    color : #117996;\n",
       "}\n",
       ".exE::before {\n",
       "    background-color: #b2dbea;\n",
       "    content:\"Exercise. \" attr(title);\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "    color : #117996;\n",
       "}\n",
       ".hint {    \n",
       "    background-color: #dede6f80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "    color : ##37665f;\n",
       "}\n",
       ".hint::before {\n",
       "    background-color: #dede6f;\n",
       "    content:\"Indice(s). \" attr(title);\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "    color : ##37665f;\n",
       "}\n",
       ".hintE {    \n",
       "    background-color: #dede6f80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "    color : ##37665f;\n",
       "}\n",
       ".hintE::before {\n",
       "    background-color: #dede6f;\n",
       "    content:\"Hint(s). \" attr(title);\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "    color : ##37665f;\n",
       "}\n",
       ".app {    \n",
       "    background-color: #b2dbea80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "}\n",
       ".app::before {\n",
       "    background-color: #b2dbea;\n",
       "    content:\"Application\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".appE {    \n",
       "    background-color: #b2dbea80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    color : #117996;\n",
       "    position:relative;\n",
       "}\n",
       ".appE::before {\n",
       "    background-color: #b2dbea;\n",
       "    content:\"Application\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    color : #117996;\n",
       "    display: block;\n",
       "}\n",
       ".solFr {    \n",
       "    background-color: #bbeab880;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "}\n",
       ".solFr::before {\n",
       "    background-color: #bbeab8;\n",
       "    content:\"Solution\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}.sol {    \n",
       "    background-color: #bbeab880;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "}\n",
       ".sol::before {\n",
       "    background-color: #bbeab8;\n",
       "    content:\"Answer\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       ".com {    \n",
       "    background-color: #ffff7f80;\n",
       "    padding: 0.5em;\n",
       "    padding-top: 0em;\n",
       "    position:relative;\n",
       "}\n",
       ".com::before {\n",
       "    background-color: #ffff7f;\n",
       "    content:\"Comment.\";\n",
       "    margin-left:-0.5em;\n",
       "    margin-right:-0.5em;\n",
       "    padding-left:0.5em;\n",
       "    padding-right:0.5em;\n",
       "    font-weight: 600;\n",
       "    display: block;\n",
       "}\n",
       "div.todo:before {\n",
       "    content:url(data:image/png;base64,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);\n",
       "    float:left;\n",
       "    margin-left:20px;\n",
       "    margin-right:20px;\n",
       "    margin-top:20px;\n",
       "    margin-bottom:20px;\n",
       "}\n",
       "div.todo{\n",
       "    font-size: 1.1em;\n",
       "    margin-top:40px;\n",
       "    background-color: #b2dbea80;\n",
       "}\n",
       "div.todo ul{\n",
       "    margin: 0.2em;\n",
       "}\n",
       "div.todo li{\n",
       "    margin-left:60px;\n",
       "    margin-top:0;\n",
       "    margin-bottom:0;\n",
       "}\n",
       "\n",
       "figure {\n",
       "    border: 0px;\n",
       "    text-align: center;\n",
       "    margin: 5px; /* adjust as needed */\n",
       "    display: block;\n",
       "    margin: auto;\n",
       "}\n",
       "figure img {\n",
       "    vertical-align: top;\n",
       "    text-align: center;\n",
       "    display: block;\n",
       "    margin: auto;\n",
       "}\n",
       "figure figcaption {\n",
       "    border: 0px;\n",
       "    text-align: center;\n",
       "    font-size: 11px;\n",
       "    margin: auto;\n",
       "}\n",
       "\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Start at:** Sunday 10 November 2024, 21:42:57  \n",
       "**Hostname:** device-11.home (Linux)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<p style=\"text-align: center\"><img width=\"800px\" src=\"./config/svg/pyPhysChemBanner.svg\" style=\"margin-left:auto; margin-right:auto\"/></p>"
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       "<IPython.core.display.Markdown object>"
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   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "cwd0 = './config/'\n",
    "sys.path.append(cwd0)\n",
    "\n",
    "import visualID_Eng as vID\n",
    "from visualID_Eng import color\n",
    "vID.init(cwd0)"
   ]
  },
  {
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   "source": [
    "# Table of Contents\n",
    "\n",
    "## Quelques pré-requis / Some prerequisite\n",
    "\n",
    "### \"Python in the Physical Chemistry Lab (PPCL)\" in a nutshell \n",
    "\n",
    "[Jupyter notebook](./PPCL.ipynb). *in English*\n",
    "\n",
    "### Constantes physiques et mathématiques\n",
    "\n",
    "<span style='color:red'>**work in progress**</span>\n",
    "\n",
    "[Jupyter notebook](./Constantes.ipynb). en Français / *in French*\n",
    "\n",
    "### Dérivées, intégrales et primitives, équations différentielles\n",
    "\n",
    "<span style='color:red'>**work in progress**</span>\n",
    "\n",
    "- **1.** Dérivées, intégrales et primitives. [Jupyter notebook](./DeriveesIntegrales1.ipynb). en Français / *in French*\n",
    "- **2.** Équations différentielles. [Jupyter notebook](./DeriveesIntegrales2.ipynb). en Français / *in French*\n",
    "\n",
    "### Computer Algebra System with Python (pCAS) in a nutshell\n",
    "\n",
    "[Jupyter notebook](./pCAS.ipynb). *in English*\n",
    "\n"
   ]
  },
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    "<div class=\"intro\">\n",
    "\n",
    "## Physical chemistry\n",
    "\n",
    "### General topics\n",
    "\n",
    "Physical Chemistry Problems and Solutions notebook. [Jupyter notebook](./PhysChem_ProblemsAndSolutions.ipynb). *in English*\n",
    "</div>\n"
   ]
  },
  {
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    "<div class=\"intro\">\n",
    "\n",
    "## Quantum chemistry\n",
    "\n",
    "### Atoms\n",
    "\n",
    "Hydrogen atom wavefunctions. [Jupyter notebook](./HydrogenAtom.ipynb). *in English*\n",
    "\n",
    "### Molecular dynamics\n",
    "\n",
    "Velocity Verlet algorithm applied to simple 1D and 2D potentials. [Jupyter notebook](./MD_VVERLET_Student.ipynb). *in English*\n",
    "\n",
    "### Spectroscopy\n",
    "\n",
    "The quantum harmonic oscillator, a \"basic\" model to interpret the vibrational properties of molecules. [Jupyter notebook](./Harmonic_Oscillator.ipynb). *in English*\n",
    "\n",
    "2nd-order NMR. [Jupyter notebook](./NMR.ipynb). *in English*\n",
    "\n",
    "### Solid-state\n",
    "\n",
    "[Tight-Binding Approach : fundamentals. A physicist point of view for theoretical chemists (pdf)](./MiscPDF/Theory-TB.pdf)\n",
    "\n",
    "[Parametrized tight-binding for calculating a material's energy and electronic structure](./TB.ipynb). *in English*\n",
    "\n",
    "### Quantum computing\n",
    "\n",
    "[Modèle d'Ising par les portes quantiques](QCpy-1(FR)-Modele_d-Ising.ipynb) ([cf aussi l'annexe](QCpy-1(FR)appendix-Modele_d-Ising.ipynb))\n",
    "\n",
    "</div>\n"
   ]
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    "<div class=\"intro\">\n",
    "\n",
    "## Traitement statistique de données (data science pour débutants) & introduction à l'apprentissage automatique / *Statistical treatment of data (data science for beginners) and introduction to machine learning*\n",
    "\n",
    "### Context\n",
    "</div>"
   ]
  },
  {
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   "execution_count": 3,
   "id": "aebd98f6-3c74-4386-b84c-0e5f3b0f84cf",
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       "\n",
       "        <iframe\n",
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       "            frameborder=\"0\"\n",
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   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "IFrame(\"./DS4B-Slides/intro.pdf\", width=1280, height=800)"
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   "source": [
    "<div class=\"intro\">\n",
    "\n",
    "### Notebooks\n",
    "\n",
    "- [Introduction](./DS4B-TOC.ipynb). en Français / *in French*\n",
    "- **1.** [Lecture et analyse de la base de données \"iris\" par la bibliothèque pandas <br> *Reading and analyzis of the \"iris\" database with the pandas library*](./DS4B-Iris1.ipynb). en Français & en Anglais / *in French and in English*\n",
    "    - [Exercices/*Exercises*.](./DS4B-Exercices/DS4B-Iris1-Exercice.ipynb)\n",
    "    - [Solution](./DS4B-Exercices/DS4B-Iris1-Correction-Exercice.ipynb)\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "- **2.** [Statistiques et régression <br> *Statistics and regression*](./DS4B-Iris2.ipynb). en Français & en Anglais / *in French and in English*\n",
    "    - [Exercices.](./DS4B-Exercices/DS4B-Iris2-Exercice.ipynb)\n",
    "    - [Corrigé.](./DS4B-Exercices/DS4B-Iris2-Correction-Exercice.ipynb)\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "- **3.** [*Basics of Artificial Neural Networks for supervised learning*](./DS4B-BasicsOfANN.ipynb). *in English*\n",
    "<br>\n",
    "- **3'.** [Apprentissage supervisé (supervised Machine Learning) appliqué à la classification (régression logistique) <br> *Supervised Machine Learning applied to classification*](./DS4B-Iris3.ipynb). en Français & en Anglais / *in French and in English*\n",
    "    - [Exercices.](./DS4B-Exercices/DS4B-Iris3-Exercice.ipynb)\n",
    "    - [Corrigé.](./DS4B-Exercices/DS4B-Iris3-Correction-Exercice.ipynb)\n",
    "<br>\n",
    "<br>\n",
    "- **4.** [*e**X**plainable **AI** (XAI)*](./DS4B-Iris4.ipynb). *in English*\n",
    "<br>\n",
    "- **5.** [*Unsupervised Machine Learning*](./DS4B-IrisUML.ipynb). *in English*\n",
    "</div>\n",
    "<br>\n",
    "<br>\n",
    "<div class=\"intro\">\n",
    "\n",
    "## Machine Learning (ML) in the Physical Chemistry lab\n",
    "\n",
    "### Machine learning in chemistry. Short lecture\n",
    "\n",
    "[pdf](./DS4B-Slides//MLChem.pdf)\n",
    "\n",
    "### Molecular representations\n",
    "\n",
    "- [3D representation & RDKit, a toolkit for cheminformatics](./MolecularRepresentations.ipynb). *in English*\n",
    "\n",
    "### Regression with ML: Prediction of the solubility of CO<sub>2</sub> in ionic liquids. *in English*\n",
    "\n",
    "- **1.** [Prediction by Support Vector Regression (SVR)](./DS4B-CO2_solubility-SVR.ipynb). *in English*\n",
    "    - [Exercise. Application of an SVR model with optimized hyperparameters](./DS4B-Exercices/DS4B-CO2_solubility-SVR-Exercise.ipynb)\n",
    "    - [Solution to the previous exercise](./DS4B-Exercices/DS4B-CO2_solubility-SVR-ExerciseWithAnswer.ipynb)\n",
    "\n",
    "- **2.** [Prediction by an Artificial Neural Network (ANN)](./DS4B-CO2_solubility-ANN.ipynb). *in English*\n",
    "    - [Exercise. Assessment of the stability of the original ML algorithm of Song *et al*. by *K*-fold cross validation](./DS4B-Exercices/DS4B-CO2_solubility-ANN-Exercise.ipynb)\n",
    "    - [Solution to the previous exercise](./DS4B-Exercices/DS4B-CO2_solubility-ANN-ExerciseWithAnswer.ipynb)\n",
    "\n",
    "- **3.** [Homework](./DS4B-TCCM-HW.ipynb). *in English*\n",
    "\n",
    "### Convolutional neural networks (CNN)\n",
    "\n",
    "- [1D CNN](ML-1DCNN.ipynb) *in English*\n",
    "- [2D CNN](ML-2DCNN.ipynb) *in English*\n",
    "\n",
    "### Recurrent neural networks (RNN)\n",
    "\n",
    "[Jupyter notebook 1](ML-RNN.ipynb) *in English*\n",
    "[Jupyter notebook 2](ML-RNN_2.ipynb) *in English*\n",
    "\n",
    "### Autoencoders\n",
    "\n",
    "[Jupyter notebook](./ML-Autoencoders.ipynb). *in English*\n",
    "\n",
    "### Misc.\n",
    "- [Annexes / *Appendixes*](./DS4B-Appendix.ipynb)\n",
    "- [\"Talktorials\" in physical chemistry and data science, Pau (France), 2023](./Misc-Slides/pyPhysChem.pdf)\n",
    "\n",
    "</div>"
   ]
  },
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   "source": [
    "<div class=\"rqT\" title=\"Advanced(?) python commands\">\n",
    "\n",
    "[Advanced python notebook](AdvancedPython.ipynb)\n",
    "</div>"
   ]
  },
  {
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   "execution_count": null,
   "id": "2667988e-7e85-46e1-b99e-8e48ca12d92b",
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