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--- tags: Jupyter, Enseignement, Python, SaclAI-School langs: fr-fr breaks: false --- # Séquençage pédagogique Cours Intro Python [Contexte et spécifications](https://codimd.math.cnrs.fr/wYsoxRgNQ5i2l1yGVvAOuw?edit) ## TODO - [x] Adapter les définitions des variables à python. - [x] Apparté sur les variables en C++ : le mettre après les exercices sur les variables. - [x] Le bloc sur les commentaires doit être un apparté. - [x] La partie type : une variable n'a pas de type en python. - [x] Retirer les variables dans la partie sur les types. - [x] Echanger les parties type et varibale. - [x] Déplacer l'apparté sur les = et == dans les variables. - [ ] Revue des feuilles existantes - [x] Implanter les [petits retours du cours d'optométrie](https://codimd.math.cnrs.fr/blGdWf1wQ_GjFK_3n2wCZg?edit) - [x] Remplacer tous les tabs par des espaces dans tous les fichiers - [x] Parcourir la liste des thèmes dans le séquençage pédagogique pour marquer ce qui est fait ou en cours - [ ] Annotations objectifs pédagogiques des feuilles existantes (voir les métadonnés en tête du fichier). Voir le glossaire dans le fichier `books/intro-prog/book/extensions/semantic.yml` (sera déplacé dans les temps qui viennent dans `src/ontology.yml`) et celui du [cours Info111](https://gitlab.dsi.universite-paris-saclay.fr/Info111/Info111/-/blob/master/_config.yml?ref_type=heads) - [ ] Revoir le parcours pédagogique par défaut pour lisser la progression - [ ] Où mettre des informations sur la mémoire, la représentation des données? - [ ] Enlever la définition de programme pour la garder pour plus tard. Où? - [ ] Aller voir le grenier (notamment les exercices embranchements) - [ ] Jupylates - [ ] Relire rapidement les exercices Jupylates - [ ] Éléonore: vérifier que tout est au bon format, supprimer les métadonnées inutiles (ex: liste de tags vide), vérifier que l'exercice peut être ouvert; compléter les objectifs pédagogiques - [ ] Nicolas: relire les learning objectives - [ ] Voir où intégrer des exercices Jupylates dans les feuilles existantes, voir convertir certains exos en exos Jupylates - [ ] Clarifier/définir la notion de données; typiquement en début du chapitre sur les structures de données - [ ] Rédiger quelques thèmes supplémentaires: dictionnaires, entrées sorties - [ ] Autour du cours - [ ] (Nicolas) Ajouter un guide pour l'enseignant: suggestion sur comment utiliser les ressources pédagogiques: organisation pratique, scénarisation - [ ] (Nicolas) Ajouter une section sur les utilisations du cours et retours? ### Administratif - [x] Lancer le contrat no 3 ## Choix pédagogiques TODO: déplacer dans la page d'accueil du cours Ce cours vise à fournir une collection modulaire d'activités réutilisables. Un circuit guidé générique créé manuellement est proposé dans l'index principal `index.md`, pour en faire un cours autonome. L'objectif à terme est d'avoir un outil de recommandation qui proposera à l'étudiant un circuit guidé personalisé en fonction de son apprentissage. D'où l'accent mis sur l'annotation sémantique des activités. Pour faciliter la réutilisation et la recherche d'activités, les activités sont classés par grande notion (boucles, fonctions, ...) avec des activités de plusieurs niveaux dans chaque sujet. Le circuit guidé, même s'il a des grandes thématiques, ne suit pas strictement ce classement. Cela afin d'assurer un tuilage en revenant plusieurs fois sur chaque grande notion (enseignement spiralé). Il n'est pas non plus découpé en séances pour s'adapter à des usages divers (synchrone, asynchrone, ...). Dans le circuit proposé, ainsi que dans un certain nombre d'acctivités, certains choix pédagogiques sont faits: - Introduire boucles while et conditionnelles rapidement - Introduire puis ancrer les fonctions au plus vite - Repousser autant que possible l'usage d'affichages Motivation: un obstacle pédagogique classique est la confusion entre afficher et renvoyer. En minimisant les affichages et introduisant les fonctions au plus tôt, on repousse le moment où la confusion peut intervient à un moment où l'apprenant est plus mature. Et a minima, on ancre comme réflexe d'utiliser plutôt un renvoi ce qui est la bonne réponse dans la plupart des cas. :::warning On a cependant besoin de tracer l'exécution d'un programme : affichage, ou effet de bord (la fourmi qui se déplace), ou alors exécution pas à pas et debug très tôt (c'est intégré dans jupyter). A explorer quand on introduit les boucles. ::: Score / meilleur score par exercice (pour les exercices avancés) Chaque séance : des exercices sur les nouveaux concepts, + des exercices aléatoires qui permettent de réviser les concepts précédents (et de les mélanger avec les nouveaux). Cours / TP mixé : comment on leur introduit les concepts, dans quel ordre ? ## Conventions d'écriture Ces conventions sont en cours de migration vers [py-edu-fr](https://foss.heptapod.net/py-edu-fr/py-edu-fr.pages.heptapod.net/-/blob/topic/default/authoring-conventions/source/contribute/authoring-conventions.md?ref_type=heads). ## Environnement de travail TODO: déplacer sur la page web du cours - [Environnement virtuel](https://mydocker.universite-paris-saclay.fr/shell/join/JGaUlNfYxAdxfheRHWHg) - [Dépôt Git](https://gitlab.dsi.universite-paris-saclay.fr/InitiationProgrammationPython/Instructors) ## Retours d'expérience ### Automne 2024: Master d'Optométrie Ce matériel pédagogique a été utilisé pour un cours d'introduction à la programmation dans un master d'optométrie à Orsay sous la forme d'un séquence de séances TPs de 2h totalisant 16h environ. La plupart du temps, il y avait deux séances successives le même jour. https://codimd.math.cnrs.fr/blGdWf1wQ_GjFK_3n2wCZg# ### Jupylates - [ ] les cellules de saisie ne sont pas configurée pour du code (colorisation, indentation automatique, ...) ## Plan du cours ### Premiers pas avec Laby (fait) [Existant](https://forge.aeif.fr/candycejupyter/instructors/-/tree/main/Sources/PremiersPas?ref_type=heads) Concepts : Initiation aux boucles, fonctions, conditionnelles, (variables), etc. Question: faire une section séparée pour les premiers pas? Ou répartir les premiers pas dans leurs thèmes respectifs, et s'appuyer sur le mécanisme de spiralage pour constuire la première séance? ### Calculer: valeurs, types, variables, initiation aux fonctions (fait) Refs: - [Cours C++](https://nicolas.thiery.name/Enseignement/Info111/Devoirs/Semaine2/index.html) - ... 1. premiers calculs, valeurs et types; potentiellement verbatim de [01-valeurs-types](https://nicolas.thiery.name/Enseignement/Info111/Devoirs/Semaine2/01-valeurs-types.html). Différences: - utiliser explicitement `type(...)` - introduire les notions d'expression, de valeur, de type. Sous forme d'encart? 2. variables (d'après 02-variable d'info 111?), mais avec l'analogie des étiquettes et non des boîtes. Voir début du [tutoriel sur les objets de Sage](https://doc.sagemath.org/html/en/thematic_tutorials/tutorial-objects-and-classes.html#tutorial-objects-and-classes) 3. modèle mémoire? Collection de petits exercices 4. initiation aux fonctions Le minimum nécessaire pour les fonctions. une fonction permet de faire un petit programme. commencer par faire des petites fonctions qui font des calculs simples. ### S'adapter au contexte: les conditionnelles (fait) - Simple - Avec else - Conditions complexes (and, or) - filées avec plusieurs cas - conditions et return ### Premiers affichages (fait) ### Boucles (fait) ### Fonctions (fait) ### Tableaux (fait) ### Compréhensions et itérateurs (en cours) Refs: - [Tutoriel thématique Sage](https://doc.sagemath.org/html/en/thematic_tutorials/tutorial-comprehensions.html#tutorial-comprehensions) - [Fin fiche 2_python du Cours ISD](https://gitlab.dsi.universite-paris-saclay.fr/L1InfoInitiationScienceDonnees/Instructors/-/blob/main/Semaine1/2_python.md?ref_type=heads) ### Dictionnaires (à faire) ## Entrées sorties (à faire) ### Fichiers (à faire) ### Bonnes pratiques (à faire) - [Séparation code / carnet](https://gitlab.dsi.universite-paris-saclay.fr/L1InfoInitiationScienceDonnees/Instructors/-/blob/main/Semaine3/1_bonnes_pratiques.md?ref_type=heads ) - Écriture de programme et exécution depuis le terminal ### Numpy (à faire) Voir ce qui est récupérable des cours L1 [MethNum](https://methnum.gitlab.dsi.universite-paris-saclay.fr/) ([dépôt](https://gitlab.dsi.universite-paris-saclay.fr/MethNum/L1)), CalculScientifique, Pierre Poulain. ### Pandas (à faire) [ISD](https://gitlab.dsi.universite-paris-saclay.fr/L1InfoInitiationScienceDonnees/Instructors/-/blob/main/Semaine1/3_pandas.md?ref_type=heads) ## Grenier ### TP1 : introduction à Python Créer un environnement jupyter, Retour sur le concept des variables, types de données, opérations sur les données. Structures : Listes, opérations sur les listes, accéder aux éléments d'une liste, tuples, dictionnaires. ### TP2 : Déroulement d'un programme If, boucles, fonctions et retours, durée de vie et étendue d'une variable, bonnes pratiques (commenter, documentation, tester le code, gérer les erreurs et les exceptions avec try catch etc). Modules, packages : Importer un module, introduction aux modules python préexistant (math, random, etc), Créer des modules personnalisés. Structures avancées : compréhension de liste en python, dictionnaire, opérations avancées, expressions génératrices. Les sets (union, intersection, difference), applications pratiques. Experssions génératrices \url{https://docs.python.org/fr/3/howto/functional.html} ### TP3 : programmation orientée objet Introduction à l'OOP, classes et objetcs, constructeurs, héritage. Concepts avancés : Class methods and static methods, Encapsulation and data hiding, practical exercices (démineur ?) ### TP4 : concepts avancés Gestion des fichiers : lire et écrire des fichiers en python, travailler avec différents formats de fichier (csv, json, etc). Librairies : numpy, pandas, matplotlib, etc. Utilisation. Peut-être présenter des librairies personnalisées selon le sujet du projet que l'étudiant aura choisi ? ### TP5 : projet personnalisé ## Idées de projets ### Projet data sciences Récuperer un challenge sur kaggle ou codalab Sentiment Analysis Tool for Social Media Posts Develop a tool for analyzing the sentiment of social media posts (e.g., tweets, Facebook posts, Reddit comments) using NLP techniques. The project involves building a model to classify the sentiment of text data into categories such as positive, negative, or neutral, based on the emotions expressed in the posts. Users can input social media text data, and the tool will output sentiment analysis results for each post. Key Features: Data Collection: Collect social media text data from platforms like Twitter, Facebook, or Reddit using APIs or web scraping techniques. Text Preprocessing: Preprocess the text data by tokenizing, removing stop words, and performing stemming/lemmatization to prepare it for sentiment analysis. Sentiment Classification Model: Train a machine learning model (e.g., Naive Bayes, Support Vector Machine, or deep learning-based models like LSTM) to classify text into sentiment categories (positive, negative, neutral). Use labeled datasets for training and validation, and fine-tune the model parameters for optimal performance. Sentiment Analysis: Implement algorithms to apply the trained model to new social media text data and predict the sentiment of each post. Generate sentiment analysis results, including sentiment scores or labels, for each post. User Interface (Optional): Develop a simple graphical user interface (GUI) using Python libraries like Tkinter or PyQt for users to input social media text data and view sentiment analysis results. Potential Extensions: Emotion Detection: Extend the tool to detect specific emotions (e.g., joy, sadness, anger) expressed in social media posts, in addition to sentiment classification. Real-Time Analysis: Implement functionality to perform sentiment analysis on live-streaming social media data and update results in real-time. Topic Modeling: Incorporate topic modeling techniques (e.g., Latent Dirichlet Allocation) to identify and analyze topics discussed in social media conversations. Learning Objectives: Gain understanding and practice in natural language processing (NLP) techniques such as text preprocessing, sentiment analysis, and machine learning classification. Learn how to design and implement applications for analyzing social media text data using Python and NLP libraries such as NLTK or spaCy. Develop problem-solving skills by addressing challenges in sentiment analysis, including data preprocessing, feature extraction, and model selection. Gain experience in building user-friendly interfaces and integrating NLP functionalities into practical tools for real-world applications. \textbf{Movie Recommendation System} Create a movie recommendation system that suggests movies to users based on their preferences and viewing history. The project involves analyzing movie ratings and user interactions to generate personalized recommendations. Utilize Python libraries such as Pandas, NumPy, scikit-learn, and Surprise for data preprocessing, collaborative filtering, and recommendation algorithms. Key Features: Data Collection and Preprocessing: Obtain movie ratings and user interactions data from sources like MovieLens or IMDb. Preprocess the data by handling missing values, encoding categorical variables, and normalizing ratings. Exploratory Data Analysis (EDA): Explore the distribution of movie ratings, user demographics, and genres. Visualize movie popularity, user preferences, and rating trends using plots and charts. Collaborative Filtering: Implement collaborative filtering algorithms such as user-based or item-based collaborative filtering. Use similarity metrics (e.g., cosine similarity, Pearson correlation) to find similar users or items based on their rating patterns. Content-Based Filtering: Incorporate content-based filtering techniques to recommend movies based on their features (e.g., genre, actors, directors). Build movie profiles using textual data (e.g., movie descriptions) and apply similarity measures to find related movies. Hybrid Recommendation System: Combine collaborative filtering and content-based filtering approaches to create a hybrid recommendation system. Use ensemble methods or weighted averages to combine individual recommendation scores from different algorithms. User Interface (Optional): Develop a web-based or desktop application for users to interact with the recommendation system. Allow users to input their preferences, view personalized recommendations, and provide feedback on recommended movies. Potential Extensions: Real-Time Recommendations: Implement real-time recommendation updates based on user interactions and feedback. Context-Aware Recommendations: Incorporate contextual information such as time of day, location, or device type to provide more relevant recommendations. Multi-Armed Bandit Approach: Apply multi-armed bandit algorithms to balance exploration and exploitation in recommendation selection. Learning Objectives: Gain hands-on experience in data preprocessing, exploratory data analysis, and recommendation system development using Python. Understand the principles and algorithms behind collaborative filtering, content-based filtering, and hybrid recommendation systems. Learn how to evaluate recommendation system performance using metrics such as precision, recall, and mean average precision. Develop skills in building user interfaces and deploying recommendation systems for practical use. \textbf{Predictive Maintenance for Industrial Equipment} Develop a predictive maintenance model using machine learning algorithms to predict equipment failures in industrial settings. The project involves analyzing historical sensor data from machinery to identify patterns and indicators of potential failures. Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data preprocessing, feature engineering, and model building. Key Features: Data Collection and Preprocessing: Collect historical sensor data from industrial equipment, including temperature, pressure, vibration, and other relevant metrics. Preprocess the data by handling missing values, outliers, and data normalization. Feature Engineering: Extract relevant features from sensor data, such as rolling averages, standard deviations, and time-series features. Create additional features based on domain knowledge and insights from the data. Model Selection and Training: Experiment with different machine learning algorithms such as Random Forest, Gradient Boosting, or Deep Learning models (e.g., LSTM). Split the dataset into training and testing sets for model evaluation. Train the models on the training data and tune hyperparameters using techniques like cross-validation. Predictive Maintenance Model: Develop a predictive maintenance model that predicts the likelihood of equipment failure within a certain time window (e.g., days or weeks). Utilize classification algorithms to classify equipment as "healthy" or "faulty" based on the predicted probability of failure. Model Evaluation and Validation: Evaluate the performance of the predictive maintenance model using metrics such as accuracy, precision, recall, and F1-score. Validate the model's predictions using real-time or historical maintenance records to assess its effectiveness in predicting actual failures. Deployment and Monitoring: Deploy the trained model into production environments for real-time monitoring of equipment health. Implement alerting systems to notify maintenance personnel when the model detects potential failures or anomalies. Continuously monitor the model's performance and update it with new data to maintain accuracy and reliability over time. Potential Extensions: Anomaly Detection: Incorporate anomaly detection techniques to identify unusual patterns or deviations from normal equipment behavior. Failure Root Cause Analysis: Integrate additional data sources such as maintenance logs, repair history, and environmental factors for root cause analysis of equipment failures. Predictive Maintenance Scheduling: Develop optimization algorithms to schedule maintenance activities based on predicted failure probabilities and cost considerations ### Projet web \textbf{Blogging Platform} Create a web-based platform for users to create, publish, and manage their blogs. The platform should include features such as user authentication, blog post creation/editing, comment sections, and user profiles. Utilize a web framework like Django or Flask for backend development and HTML/CSS/JavaScript for frontend design. Key Features: User authentication system with login/logout functionality. Dashboard for users to manage their blog posts, drafts, and comments. Rich text editor for composing blog posts with formatting options. Comment section for users to engage with blog posts and discussions. User profiles with bio, profile picture, and social media links. Search functionality to find blog posts by title, author, or keywords. Responsive design for mobile and desktop devices. \textbf{Online Bookstore} Develop an online bookstore where users can browse, search, and purchase books from various genres. Implement features such as user registration, book catalog with detailed descriptions, shopping cart functionality, and secure payment processing. Utilize a web framework like Django along with a database management system like PostgreSQL. Key Features: User registration and authentication system for creating accounts and managing orders. Book catalog with categories, authors, ratings, and reviews. Search and filtering options to find books by title, author, genre, or keyword. Shopping cart functionality to add/remove items and calculate total price. Secure payment processing using third-party payment gateways (e.g., Stripe, PayPal). Order management system for tracking order status and history. Responsive design for seamless user experience on all devices. \textbf{Recipe Sharing Platform} Develop a web-based platform for users to share and discover recipes. Users can create profiles, upload their own recipes, browse recipes by category or cuisine, and interact with other users through comments and ratings. Utilize a web framework like Django for backend development and integrate a frontend framework like React or Vue.js for dynamic user interfaces. Key Features: User authentication system with profile creation and customization options. Recipe database with categories, tags, and filtering options. Upload and share recipes with ingredients, instructions, and photos. Search functionality to find recipes by title, ingredients, or tags. User interaction features such as comments, likes, and ratings for recipes. Bookmarking system for users to save their favorite recipes. Responsive design for optimal viewing experience across devices. \textbf{Fitness Tracker and Workout Planner} Create a web-based application for users to track their fitness activities, set goals, and plan workouts. Users can log their exercises, track progress over time, and receive personalized recommendations based on their fitness goals. Utilize a web framework like Flask or Django for backend development and integrate a frontend framework like Angular or React for interactive user interfaces. Key Features: User registration and authentication system with profile management. Dashboard with overview of fitness goals, progress charts, and upcoming workouts. Exercise database with predefined workouts and customization options. Log workouts with details such as exercise type, duration, intensity, and calories burned. Progress tracking with charts and graphs showing improvements over time. Personalized recommendations for workouts, based on user preferences and goals. Integration with fitness trackers or wearables for automatic data syncing. \textbf{Online Learning Platform} Build a web-based platform for hosting online courses, tutorials, and educational resources. Users can sign up for courses, access learning materials, participate in quizzes and assignments, and interact with instructors and fellow students. Utilize a web framework like Django for backend development and integrate a frontend framework like React or Vue.js for interactive user interfaces. Key Features: User authentication system with profile creation and customization options. Course catalog with categories, tags, and filtering options. Course enrollment and progress tracking for users. Lecture materials including videos, slides, and text-based content. Quizzes, assignments, and assessments to test understanding and progress. Discussion forums and messaging features for communication with instructors and peers. Certification and achievement badges upon completion of courses. ### Projet bioinfo \url{https://webusers.i3s.unice.fr/~bernot/Enseignement/GB3_Python1/cours00.pdf} \textbf{DNA Sequence Analysis Tool} Develop a Python program for analyzing DNA sequences obtained from biological data. The program should include functionalities such as sequence alignment, motif search, translation to amino acids, and identification of genetic variations (e.g., single nucleotide polymorphisms). Visualize the results to provide insights into genetic information and biological processes. Key Features: Input and manipulation of DNA sequences in various formats (FASTA, GenBank). Sequence alignment algorithms (e.g., Smith-Waterman, Needleman-Wunsch) for comparing sequences. Motif search using regular expressions or bioinformatics libraries (e.g., Biopython). Translation of DNA sequences into amino acid sequences (protein sequences). Analysis of genetic variations, including identification of mutations and SNP analysis. Visualization tools for displaying sequence alignments, motifs, and genetic variations. \textbf{Phylogenetic Tree Construction} Create a Python program for constructing phylogenetic trees based on molecular sequence data (e.g., DNA or protein sequences). Implement algorithms such as neighbor-joining or maximum likelihood to infer evolutionary relationships between different species or organisms. Visualize the phylogenetic tree to illustrate the evolutionary divergence and relatedness. Key Features: Input and preprocessing of molecular sequence data from multiple species or taxa. Implementation of phylogenetic tree construction algorithms (e.g., neighbor-joining, maximum likelihood). Calculation of evolutionary distances between sequences using substitution models (e.g., Jukes-Cantor, Kimura). Visualization of phylogenetic trees using tree plotting libraries (e.g., Phylo, ETE Toolkit). Annotation of nodes and branches with bootstrap support values and branch lengths. Comparison of phylogenetic trees to study evolutionary relationships and divergence patterns. \textbf{Population Genetics Simulation} Build a Python program for simulating evolutionary processes and population genetics phenomena. The program should model genetic drift, natural selection, gene flow, and mutation within populations over multiple generations. Analyze the effects of different parameters on allele frequencies and population dynamics. Key Features: Simulation of populations with specified initial allele frequencies and demographic parameters. Implementation of evolutionary processes such as genetic drift, natural selection, and mutation. Visualization of allele frequency changes over generations using line plots or histograms. Analysis tools to calculate genetic diversity indices (e.g., allele richness, heterozygosity). Comparison of simulated scenarios with theoretical expectations from population genetics models. Interactive simulation interface allowing users to adjust parameters and observe outcomes in real-time. ### Projet physique \textbf{Projectile Motion Simulator} Develop a Python program that simulates the motion of a projectile under the influence of gravity. This project will allow students to apply concepts of kinematics, vectors, and numerical methods to model the trajectory of a projectile launched at various angles and velocities. Key Features: Graphical Simulation: Implement a graphical interface using libraries like Matplotlib or Pygame to visually represent the trajectory of the projectile. User Input: Allow users to input initial parameters such as launch angle, initial velocity, and gravitational acceleration. Physics Calculation: Utilize physics equations to calculate the trajectory of the projectile, considering factors like gravity and air resistance (if desired). Visualization: Display the trajectory of the projectile in real-time as it moves through the air, updating the position at each time step. Data Analysis: Provide data analysis tools to extract information such as maximum height, range, time of flight, and impact velocity. Customization: Allow users to customize the simulation by adjusting parameters such as air density, wind speed, and projectile mass. Error Handling: Implement error handling to handle invalid inputs and prevent runtime errors. Documentation: Include detailed documentation explaining the physics principles behind the simulation and how the program works. Potential Extensions: Multiple Projectiles: Extend the simulation to handle multiple projectiles launched simultaneously or sequentially. 3D Visualization: Implement a 3D visualization option to show the projectile's motion in a three-dimensional space. Advanced Physics Models: Incorporate more complex physics models, such as accounting for drag force or non-uniform gravitational fields. Projectile Optimization: Add functionality to optimize projectile parameters (e.g., launch angle, velocity) to achieve specific goals (e.g., maximum range or height). Learning Objectives: Apply kinematic equations to model projectile motion. Understand the concept of vectors and vector addition in physics. Gain experience in numerical methods and computational physics. Enhance programming skills by developing a graphical simulation tool in Python. \textbf{Pendulum Motion Simulator} Create a Python program that simulates the motion of a simple pendulum. The program should allow users to specify parameters such as pendulum length, initial displacement, and gravitational acceleration. Visualize the motion of the pendulum over time and analyze its behavior, including period, frequency, and energy conservation. Key Features: Graphical simulation of pendulum motion using Matplotlib. User input for pendulum parameters and initial conditions. Calculation of pendulum trajectory using differential equations for simple harmonic motion. Visualization of pendulum motion and energy conservation over time. Analysis tools to calculate period, frequency, and other relevant quantities. \textbf{Solar System Simulation} Develop a Python program that simulates the motion of celestial bodies in the solar system. Utilize Newton's law of universal gravitation to model the interactions between planets, moons, and the Sun. Visualize the orbits of planets and moons over time, and analyze their dynamics and orbital characteristics. Key Features: Graphical simulation of the solar system using Matplotlib or Pygame. Input parameters for celestial bodies' masses, initial positions, and velocities. Numerical integration methods (e.g., Euler's method, Runge-Kutta) to solve differential equations of motion. Visualization of planetary orbits, including elliptical paths and orbital precession. Analysis tools to calculate orbital parameters such as eccentricity, semi-major axis, and orbital period. \textbf{Wave Interference Simulator} Build a Python program that simulates the interference patterns formed by waves, such as light waves or water waves. Use principles of wave superposition to model constructive and destructive interference phenomena. Visualize interference patterns generated by multiple wave sources and analyze their spatial distribution and intensity. Key Features: Graphical simulation of wave interference patterns using Matplotlib or Pygame. User input for wave parameters such as frequency, wavelength, and amplitude. Implementation of wave superposition principle to compute resultant wave amplitude at each point. Visualization of interference patterns, including nodal lines and intensity maxima/minima. Analysis tools to calculate interference fringe spacing, contrast ratio, and other relevant quantities. These project ideas offer opportunities for students to explore various concepts in physics while honing their programming skills in Python. Each project can be tailored to different levels of complexity, making them suitable for beginners to advanced learners. ### Projet jeu \textbf{ "Snake Game"} Create a classic Snake game where the player controls a snake that moves around the screen, eating food pellets to grow longer while avoiding collisions with walls and its own tail. The project involves implementing game mechanics, handling user input, and updating the game state based on player actions. Key Features: Snake Movement: Implement snake movement mechanics using arrow keys (up, down, left, right) to change the snake's direction. Update the snake's position on the screen based on its current direction and speed. Food Pellets: Generate food pellets at random positions on the screen. Detect when the snake collides with a food pellet and increase its length. Collision Detection: Detect collisions between the snake and walls of the game window. Detect collisions between the snake's head and its own body segments. Scoring System: Keep track of the player's score based on the number of food pellets eaten. Display the score on the screen during gameplay. Game Over Condition: End the game when the snake collides with a wall or its own body. Display a game over message and final score when the game ends. User Interface (Optional): Develop a simple graphical user interface using a Python library like Pygame for displaying game elements and handling user input. Potential Extensions: Advanced Gameplay Mechanics: Add additional features such as power-ups, obstacles, or teleportation portals to make the game more challenging. Multiplayer Mode: Implement a multiplayer mode where two players can compete or cooperate to control separate snakes on the same screen. Customization Options: Allow players to customize the appearance of the snake, food pellets, and game environment. \textbf{Minesweeper Game} Project Description: Create a classic Minesweeper game where the player uncovers tiles on a grid while avoiding hidden mines. The project involves implementing game mechanics, generating random mine placements, handling user input, and updating the game state based on player actions. Key Features: Grid Generation: Generate a grid of squares representing the game board, with each square initially hidden. Randomly place a specified number of mines within the grid. Game Mechanics: Allow the player to uncover tiles by clicking on them. Implement the rules of Minesweeper: revealing numbers indicating adjacent mines, clearing adjacent empty squares recursively, and revealing mines upon clicking on them. Displaying Information: Display numbers on revealed tiles indicating the number of adjacent mines. Show flags on tiles marked by the player as potential mines. User Input: Handle mouse clicks to reveal tiles or mark them as potential mines with flags. Implement right-click functionality for flagging tiles. Game Over Conditions: End the game if the player uncovers a mine, revealing all mines on the board. Determine win conditions when all non-mine tiles are uncovered. User Interface (Optional): Develop a simple graphical user interface using a Python library like Pygame or Tkinter for displaying the game board and handling user input. Potential Extensions: Difficulty Levels: Implement multiple difficulty levels (e.g., easy, medium, hard) with different grid sizes and mine densities. Timer and Score: Add a timer to track the time taken by the player to complete the game and calculate a score based on completion time and difficulty level. Customization Options: Allow players to customize game settings such as grid size, number of mines, and colors/themes. Learning Objectives: Gain experience in game development fundamentals such as game loops, grid manipulation, and event handling. Learn how to generate random game elements and implement game rules and mechanics. Understand basic concepts of user interface design and interactivity in game development. Develop problem-solving and programming skills through iterative game development and debugging. ### NLP \textbf{Text Summarization Tool} Develop a tool for automatically generating summaries of text documents using NLP techniques. The project involves implementing algorithms to extract key information from large text documents and generate concise summaries that capture the main points. Users can input a text document, and the tool will output a summary of the document's content. Key Features: Text Preprocessing: Implement text preprocessing techniques such as tokenization, stop word removal, and stemming/lemmatization to prepare the text for summarization. Sentence Extraction: Develop algorithms to identify important sentences in the text document based on factors such as sentence length, frequency of occurrence, and relevance to the overall topic. Feature Extraction: Extract key features or phrases from the text using techniques such as term frequency-inverse document frequency (TF-IDF) or word embeddings (e.g., Word2Vec, GloVe). Summarization Algorithm: Implement a summarization algorithm (e.g., extractive or abstractive summarization) to generate a concise summary of the text document based on the extracted sentences and features. User Interface (Optional): Develop a simple graphical user interface (GUI) using Python libraries like Tkinter or PyQt for users to input text documents and view summaries. Potential Extensions: Multi-document Summarization: Extend the tool to summarize multiple documents simultaneously and generate a combined summary. Customization Options: Allow users to specify summarization parameters such as summary length, language, or domain-specific keywords. Evaluation Metrics: Implement evaluation metrics (e.g., ROUGE, BLEU) to assess the quality and coherence of generated summaries objectively. Learning Objectives: Gain understanding and practice in natural language processing (NLP) techniques such as text preprocessing, feature extraction, and summarization algorithms. Learn how to design and implement applications for text processing and analysis using Python and NLP libraries such as NLTK or spaCy. Develop problem-solving skills by addressing challenges in automatic text summarization and improving the efficiency and accuracy of the summarization process. Gain experience in building user-friendly interfaces and integrating NLP functionalities into practical tools for real-world applications. ### Projet maths \textbf{Cryptography Toolkit} Develop a toolkit for implementing various cryptographic algorithms and techniques, allowing users to encrypt, decrypt, and analyze encrypted data. The project involves implementing algorithms for symmetric and asymmetric encryption, hashing, digital signatures, and cryptographic protocols. Key Features: Symmetric Encryption: Implement algorithms such as AES (Advanced Encryption Standard) or DES (Data Encryption Standard) for symmetric encryption. Allow users to encrypt and decrypt messages using a shared secret key. Asymmetric Encryption: Implement algorithms such as RSA (Rivest-Shamir-Adleman) or ECC (Elliptic Curve Cryptography) for asymmetric encryption. Provide tools for generating key pairs, encrypting and decrypting messages, and digital signatures. Hashing Functions: Implement hashing algorithms such as SHA-256 or MD5 for generating hash values from input data. Allow users to calculate hash values for files or messages and verify integrity using checksums. Digital Signatures: Implement algorithms for generating and verifying digital signatures using public-key cryptography. Provide tools for signing documents, verifying signatures, and ensuring authenticity and integrity. Key Management: Develop features for managing cryptographic keys, including key generation, storage, distribution, and exchange. Ensure secure handling of keys and protect against key compromise or unauthorized access. Cryptanalysis Tools: Include tools for analyzing encrypted data and performing cryptanalysis attacks such as brute force, frequency analysis, or chosen-plaintext attacks. Provide insights into the security of cryptographic algorithms and vulnerabilities. User Interface (Optional): Develop a simple command-line interface (CLI) or graphical user interface (GUI) using Python libraries like Tkinter or PyQt for users to interact with the cryptography toolkit and perform cryptographic operations. Potential Extensions: Secure Communication Protocols: Implement secure communication protocols such as SSL/TLS for encrypting data transmission over networks and ensuring privacy and security in online communication. Blockchain Technology: Explore applications of cryptography in blockchain technology, such as implementing cryptographic hash functions and digital signatures in blockchain networks. Learning Objectives: Gain understanding and practice in cryptographic algorithms, techniques, and protocols using Python. Learn how to design and implement cryptographic systems for securing data and communication channels. Develop problem-solving skills by addressing challenges in key management, cryptanalysis, and secure communication. Gain experience in building user-friendly interfaces and integrating cryptographic functionalities into practical tools for data security and privacy. \textbf{Mathematical Puzzle Solver} Develop a solver for various mathematical puzzles and challenges, allowing users to input puzzle problems and receive solutions. The project involves implementing algorithms for solving mathematical puzzles such as Sudoku, crossword puzzles, logic puzzles, and mathematical riddles. Key Features: Sudoku Solver: Implement an algorithm to solve Sudoku puzzles of varying difficulty levels. Allow users to input Sudoku grids and generate solutions using backtracking or constraint satisfaction techniques. Crossword Puzzle Solver: Develop a tool for solving crossword puzzles by filling in missing letters based on clues and intersecting words. Implement algorithms for pattern matching and word lookup in a dictionary. Logic Puzzle Solver: Create a solver for logic puzzles such as logic grid puzzles, Einstein puzzles, or logic riddles. Implement algorithms for logical deduction, inference, and constraint propagation. Mathematical Riddle Solver: Build a solver for mathematical riddles and puzzles involving arithmetic, algebra, geometry, and logic. Implement algorithms for problem-solving and mathematical reasoning to find solutions to riddles and puzzles. User Interface (Optional): Develop a simple command-line interface (CLI) or graphical user interface (GUI) using Python libraries like Tkinter or PyQt for users to input puzzle problems and view solutions. Potential Extensions: Interactive Puzzle Generator: Extend the project to include features for generating new puzzles and challenges dynamically. Allow users to customize puzzle parameters and difficulty levels. Learning Objectives: Gain understanding and practice in problem-solving strategies and algorithms for solving mathematical puzzles and challenges. Learn how to design and implement algorithms for constraint satisfaction, logical deduction, and pattern recognition. Develop analytical and critical thinking skills by tackling complex puzzles and reasoning through solutions. Gain experience in building user-friendly interfaces and integrating puzzle-solving functionalities into practical tools for entertainment and education. \textbf{Vehicle Routing Problem Solver} The Vehicle Routing Problem (VRP) is a classic problem in operations research and logistics that involves determining the optimal routes for a fleet of vehicles to deliver goods to a set of customers, while minimizing total distance traveled or total delivery time. The project involves implementing algorithms to solve the VRP and generate efficient delivery routes for vehicles. Key Features: Input Data Generation: Generate random instances of the VRP with specified parameters such as the number of vehicles, the number of customers, locations of customers, demand for goods, vehicle capacities, and distances between locations. Route Optimization Algorithms: Implement algorithms to solve the VRP, such as heuristics (e.g., nearest neighbor, savings algorithm) or metaheuristics (e.g., genetic algorithms, simulated annealing, ant colony optimization). Develop custom algorithms or adapt existing algorithms to handle VRP variants such as the capacitated VRP (CVRP), the vehicle routing problem with time windows (VRPTW), or the vehicle routing problem with pickup and delivery (VRPPD). Constraint Handling: Handle constraints such as vehicle capacity constraints, time windows for customer visits, maximum route duration, and multiple depots for vehicle departure. Visualization and Analysis: Visualize the optimized routes on a map and provide statistics such as total distance traveled, total delivery time, vehicle utilization, and customer service levels. Analyze the sensitivity of the solution to changes in input parameters and evaluate the performance of different algorithms for solving the VRP. User Interface (Optional): Develop a simple graphical user interface (GUI) using Python libraries like Tkinter or PyQt for users to input VRP parameters, visualize solutions, and analyze results. Potential Extensions: Real-World Data Integration: Extend the project to handle real-world data such as geographical coordinates of customers, traffic conditions, road networks, and delivery time windows obtained from GPS data or logistics databases. Dynamic Routing: Implement algorithms for dynamic vehicle routing where new customer orders arrive dynamically, and routes need to be adjusted in real-time to accommodate changes. Learning Objectives: Gain understanding and practice in optimization algorithms and techniques for solving combinatorial optimization problems. Learn how to model real-world problems using mathematical optimization concepts and formulate them as optimization problems. Develop problem-solving skills by addressing complex constraints and requirements in the context of the VRP. Gain experience in building algorithms and tools for logistics and operations research applications, with practical implications for transportation and supply chain management.