Estadísticas y predicciones de W15 Fiano Romano

Historial participativo del W15 Fiano Romano

A lo largo de los años, este torneo ha sido testigo del surgimiento de varias estrellas emergentes que posteriormente han escalado posiciones significativas dentro del ranking mundial femenino.

• Anastasia Mironova (2018): Ganó este torneo antes de entrar al top-100 mundial.
• Elena Petrova (2019): Se adjudicó el título justo antes de clasificar al cuadro principal del US Open.
• Sofia Delgado (2020): Conquistó Fiano Romano antes de hacer historia al alcanzar semifinales en Roland Garros como jugadora clasificada (Q).

Cada una ha dejado huella no solo por sus logros deportivos sino también por inspirar a nuevas generaciones a soñar grande dentro del circuito profesional femenino.

Análisis estadístico previo al torneo

Para entender mejor lo que podríamos esperar mañana aquí están algunos datos clave sobre el desempeño reciente y estadísticas relevantes:

• Ana Martínez tiene un índice superior al promedio en primeros servicios ganados (78%) comparado con sus rivales directas (71%). Esto le da una ventaja inicial crucial durante los puntos importantes.
• Elena Rossi ha demostrado mantener un alto nivel durante juegos largos (>10 minutos), lo cual es vital cuando se trata de remontadas o mantener ventajas significativas durante sets cerrados.
• Sofia Bianchi posee uno de los mejores porcentajes globales (+/- puntos) desde la red (+55%) entre las participantes actuales del torneo.

  Cada uno de estos datos nos da indicios sobre qué esperar durante las confrontaciones directas mañana y cómo podrían desplegarse tácticamente cada partido dependiendo del contexto específico.

  Perspectivas futuras para el circuito ITF femenino juvenil <|repo_name|>johndoe654321/NDK-Robotics<|file_sep|>/README.md # NDK-Robotics ## Abstract The goal of this project is to design and develop an open-source robotic arm that can be used in educational environments to teach students about the basic principles of robotics. ## Team Members - [Baptiste Devillers](https://github.com/baptistedevillers) - [David Faure](https://github.com/davidfaure) - [Jean-Noël Schifano](https://github.com/jnschifano) - [Yann Pichon](https://github.com/ypichon) ## Overview ### Structure of the project The project is organized into several modules: - `arm`: This module contains the code for controlling the robotic arm. - `vision`: This module contains the code for computer vision tasks such as object detection and pose estimation. - `simulation`: This module contains code for simulating the robotic arm and testing its performance. - `education`: This module contains educational materials and resources related to the project. ### Documentation The project documentation is available on [ReadTheDocs](https://ndkrobotics.readthedocs.io/en/latest/). ## Installation ### Prerequisites - Python >=3.7 - CMake >=3.16 ### Setup 1. Clone the repository: bash git clone https://github.com/NDK-Robotics/NDK-Robotics.git 2. Navigate to the project directory: bash cd NDK-Robotics 3. Create and activate a virtual environment: bash python3 -m venv venv source venv/bin/activate 4. Install dependencies: bash pip install -r requirements.txt ## Usage ### Running the simulation To run the simulation of the robotic arm: bash python simulation/main.py ### Controlling the robotic arm To control the robotic arm using Python: python from arm.controller import RoboticArmController controller = RoboticArmController() controller.move_to_position(x=100, y=200, z=300) ### Object detection with computer vision To perform object detection using OpenCV: python from vision.detector import ObjectDetector detector = ObjectDetector() image = cv2.imread('image.jpg') detections = detector.detect(image) ## Contributing We welcome contributions to this project! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) file for more information on how to contribute. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. <|file_sep|># Computer Vision Module This module provides tools and algorithms for computer vision tasks such as object detection and pose estimation. ## Object Detection The `ObjectDetector` class provides methods for detecting objects in images using pre-trained models from OpenCV. python from vision.detector import ObjectDetector detector = ObjectDetector() image = cv2.imread('image.jpg') detections = detector.detect(image) for detection in detections: print(f"Object detected at {detection['bbox']} with confidence {detection['confidence']}") ## Pose Estimation The `PoseEstimator` class provides methods for estimating the pose of an object in an image. python from vision.pose_estimator import PoseEstimator pose_estimator = PoseEstimator() image = cv2.imread('image.jpg') object_points = [...] # Define object points in real-world coordinates image_points = [...] # Define corresponding image points in pixel coordinates rvec, tvec = pose_estimator.solve_pnp(object_points, image_points) <|file_sep|># Robotic Arm Controller Module This module provides classes and methods to control the robotic arm. ## RoboticArmController Class The `RoboticArmController` class provides methods to move and control the robotic arm. python from arm.controller import RoboticArmController controller = RoboticArmController() controller.move_to_position(x=100, y=200, z=300) controller.move_to_angle(joint1=45, joint2=30) controller.open_gripper() controller.close_gripper() <|repo_name|>johndoe654321/NDK-Robotics<|file_sep|>/arm/controller.py import time class RoboticArmController: def __init__(self): # Initialize communication with hardware (e.g., via serial port) pass def move_to_position(self, x: float, y: float, z: float): """Move the robotic arm to a specific position in Cartesian coordinates.""" # Convert Cartesian coordinates to joint angles using inverse kinematics (not implemented here) joint_angles = self._cartesian_to_joint_angles(x, y, z) # Send commands to hardware to move to calculated joint angles self._move_to_joint_angles(joint_angles) time.sleep(1) # Wait for movement to complete (simplified) def _cartesian_to_joint_angles(self, x: float, y: float, z: float) -> list: """Convert Cartesian coordinates to joint angles using inverse kinematics