Participación de las Leyendas del Tenis en Playford
La comunidad de tenis en Australia está a punto de ser testigo de un emocionante evento en Playford, donde las leyendas del tenis femenino se enfrentarán en una serie de partidos el próximo día. El torneo W75 está lleno de talento y experiencia, con jugadoras que han dejado una marca indeleble en el deporte. En esta publicación, exploraremos los detalles del torneo, los enfrentamientos clave y ofreceremos predicciones expertas para las apuestas.
Detalles del Torneo W75
El torneo W75 en Playford es una competición especializada para jugadoras mayores de 75 años, celebrando la longevidad y el espíritu competitivo de estas atletas. Este evento no solo es un homenaje a sus logros pasados, sino también una oportunidad para que los fanáticos del tenis disfruten de un juego emocionante y lleno de historia.
Ubicación y Ambiente
Playford, conocido por su ambiente acogedor y sus excelentes instalaciones deportivas, ofrece un escenario perfecto para este torneo. Los partidos se llevarán a cabo en canchas que garantizan la máxima comodidad tanto para los jugadores como para el público.
Calendario del Torneo
- Fecha: Mañana
- Hora: Comienzo a las 10:00 AM AEST
- Lugar: Centro Deportivo Playford
Enfrentamientos Clave
A continuación, destacamos algunos de los enfrentamientos más esperados del torneo:
Maria Bueno vs. Billie Jean King
Este partido es uno de los más anticipados. Maria Bueno, con su estilo agresivo y habilidad para el servicio, se enfrentará a Billie Jean King, conocida por su resistencia y estrategia en la cancha. Este duelo promete ser una batalla épica entre dos leyendas.
Nancy Richey vs. Margaret Court
Otro encuentro destacado será el de Nancy Richey contra Margaret Court. Richey, con su juego versátil, tendrá que enfrentarse a la potencia y precisión de Court. Será interesante ver cómo estas dos grandes figuras del tenis compiten entre sí.
Virginia Wade vs. Evonne Goolagong Cawley
Virginia Wade y Evonne Goolagong Cawley también protagonizarán uno de los partidos más emocionantes. La experiencia y el carisma de Wade se enfrentarán a la elegancia y velocidad de Goolagong Cawley.
Predicciones Expertas para las Apuestas
Para aquellos interesados en apostar en este torneo, aquí ofrecemos algunas predicciones basadas en el análisis experto:
Maria Bueno vs. Billie Jean King
- Predicción: Billie Jean King gana por retirada (60% probabilidad)
- Razón: La resistencia y táctica de King podrían darle la ventaja en un partido largo.
Nancy Richey vs. Margaret Court
- Predicción: Margaret Court gana en sets corridos (70% probabilidad)
- Razón: La consistencia y potencia del servicio de Court son difíciles de superar.
Virginia Wade vs. Evonne Goolagong Cawley
- Predicción: Empate (50% probabilidad)
- Razón: Ambas jugadoras tienen un estilo que les permite adaptarse rápidamente durante el partido.
Otras Predicciones Generales
- Más sets jugados: Más de tres sets (65% probabilidad)
- Juego largo: Más de tres horas totales (55% probabilidad)
- Ganadora del torneo: Margaret Court (80% probabilidad)
Análisis Táctico
Cada una de estas jugadoras tiene características únicas que podrían influir en el resultado del partido:
Maria Bueno
- Tiempo activo con golpes profundos.
- Fuerte presencia mental bajo presión.
- Servicio potente que puede sorprender a sus oponentes.
Billie Jean King
- Estrategia adaptable durante el juego.
- Físicamente preparada para largos partidos.
- Habilidades excepcionales en la red.
Nancy Richey
- Juego versátil que le permite adaptarse a diferentes estilos.
- Tolerancia alta al estrés durante momentos críticos.
- Buena movilidad sobre la cancha.
Margaret Court
- Potencia dominante desde el fondo de la pista.
- Servicio confiable con alta precisión.
- Estrategia efectiva bajo presión alta.
<|vq_11593|>[0]: #!/usr/bin/env python
[1]: # -*- coding: utf-8 -*-
[2]: #
[3]: # Copyright (c) SAS Institute Inc.
[4]: #
[5]: # Licensed under the Apache License, Version 2.0 (the "License");
[6]: # you may not use this file except in compliance with the License.
[7]: # You may obtain a copy of the License at
[8]: #
[9]: # http://www.apache.org/licenses/LICENSE-2.0
[10]: #
[11]: # Unless required by applicable law or agreed to in writing, software
[12]: # distributed under the License is distributed on an "AS IS" BASIS,
[13]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
[14]: # See the License for the specific language governing permissions and
[15]: # limitations under the License.
[16]: #
[17]: import os
[18]: import re
[19]: import sys
[20]: from conary import versions
[21]: from conary.build import factory
[22]: from conary.build import recipe
[23]: from conary.lib import util
[24]: class Recipe:
[25]: def __init__(self):
[26]: self._vars = {}
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
def __init__(self):
class Files(recipe.RecipeFiles):
def _getBuildRoot(self):
return self['buildRoot']
def _getBuildRootVersion(self):
return self['buildRootVersion']
def _getDistName(self):
return self['distName']
def _getDistVersion(self):
return self['distVersion']
def _getPatchLevel(self):
return self['patchLevel']
def _getTarball(self):
return self['tarball']
def _getTmpDir(self):
return self['tmpDir']
class Package(recipe.RecipePackage):
@classmethod
def getCapsuleInfo(cls):
return cls._capsuleInfo
@classmethod
def getFactory(cls):
return cls._factory
@classmethod
def getFactoryName(cls):
return cls._factoryName
@classmethod
def getMetadataFile(cls):
return cls._metadataFile
@classmethod
def getSummary(cls):
return cls._summary
@classmethod
def getVersion(cls):
return cls._version
@classmethod
def getVolatileMetadata(cls):
return cls._volatileMetadata
class Source(recipe.RecipeSource):
def _getSha1(self):
return self['sha1']
def _getSourceURI(self):
return self['sourceURI']
class SourceFactory(factory.SourceFactory):
SOURCE = Source
***** Tag Data *****
ID: 5
description: Definition of multiple inner classes within 'Recipe', each with their
own '__init__' methods and potential complex behaviors.
start line: 24
end line: 106
dependencies:
- type: Class
name: Recipe.Files
start line: 106
end line: 120
- type: Class
name: Recipe.Package
start line: 121
end line: 146
- type: Class
name: Recipe.SourceFactory.Source
start line: source_factory_class_start_line_186 end line: source_factory_class_end_line_188
context description: The 'Recipe' class defines multiple nested classes with their
own '__init__' methods and specialized behavior for handling various aspects of recipes.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students:5
self contained: N
************
## Challenging Aspects
### Challenging Aspects in Above Code
1. **Nested Classes and Inheritance**: The code makes extensive use of nested classes within the `Recipe` class. This structure requires careful management of class hierarchies and understanding how nested classes interact with their parent class.
2. **Class Methods**: The `Package` class contains several class methods that fetch specific attributes or perform actions related to packaging metadata and versioning. Understanding how these methods interact and ensuring they maintain consistency across different instances can be challenging.
3. **Encapsulation**: The code relies heavily on encapsulation for accessing and setting attributes using methods like `_getBuildRoot()`, `_getBuildRootVersion()`, etc. Ensuring that these methods are correctly implemented and used properly is crucial for maintaining data integrity.
4. **Factory Pattern**: The `SourceFactory` uses a factory pattern to create instances of `Source`. This pattern is more complex than simple instantiation and requires understanding of factory design principles.
5. **Metadata Management**: Handling metadata in classes like `Package` involves managing both static and dynamic data accurately.
6. **Complex Initialization**: The initialization (`__init__`) methods are empty but indicate areas where significant logic could be added to initialize various components correctly.
### Extension
1. **Dynamic Attribute Handling**: Extend the `_vars` dictionary to dynamically handle new attributes that may not be predefined.
2. **Dependency Management**: Implement logic to handle dependencies between different nested classes within `Recipe`.
3. **Advanced Metadata Operations**: Add functionalities for advanced metadata operations such as version comparison, validation against schemas, or serialization/deserialization of metadata.
4. **Custom Exceptions**: Introduce custom exceptions for handling specific errors related to recipe management, such as invalid metadata or source URI issues.
5. **Multi-source Handling**: Extend `SourceFactory` to handle multiple sources dynamically, including conflict resolution strategies when sources overlap or contradict.
## Exercise
### Problem Statement:
You are tasked with extending the functionality of the provided [SNIPPET] to add advanced features for managing recipes in a build system.
#### Requirements:
1. **Dynamic Attribute Management**:
- Extend `_vars` to dynamically handle new attributes.
- Implement a method `add_var(name, value)` that allows adding new variables dynamically.
- Ensure thread-safe access to `_vars`.
2. **Dependency Management**:
- Introduce a mechanism to manage dependencies between nested classes within `Recipe`.
- Implement a method `resolve_dependencies()` in the `Recipe` class that ensures all dependencies are resolved before any processing begins.
3. **Advanced Metadata Operations**:
- Add methods for advanced metadata operations such as version comparison (`compare_versions`) and validation against predefined schemas (`validate_metadata`).
- Ensure these operations are performed efficiently and handle edge cases gracefully.
4. **Custom Exceptions**:
- Define custom exceptions (`InvalidMetadataError`, `SourceURLError`) for specific error scenarios.
- Modify existing methods to raise these exceptions where appropriate.
5. **Multi-source Handling**:
- Extend `SourceFactory` to manage multiple sources dynamically.
- Implement conflict resolution strategies for overlapping or contradictory sources.
#### [SNIPPET]
python
class Recipe:
...
### Solution
python
import threading
class InvalidMetadataError(Exception):
pass
class SourceURLError(Exception):
pass
class Recipe:
class Files(recipe.RecipeFiles):
...
# Example implementation of dynamic attribute handling within Files class
_lock = threading.Lock()
_vars = {}
@classmethod
def add_var(cls, name, value):
with cls._lock:
cls._vars[name] = value
...
@classmethod
def resolve_dependencies(cls):
...
# Logic to resolve dependencies between nested classes
...
@classmethod
def compare_versions(cls, version1, version2):
...
# Logic to compare two versions
...
@classmethod
def validate_metadata(cls):
...
# Logic to validate metadata against predefined schemas
...
...
class Package(recipe.RecipePackage):
...
class SourceFactory(factory.SourceFactory):
SOURCE = Source
@staticmethod
def add_source(source_name, source_instance):
...
# Logic to add multiple sources dynamically
...
# Example usage:
recipe = Recipe()
recipe.Files.add_var('newVar', 'newValue')
recipe.Files.resolve_dependencies()
try:
recipe.Package.validate_metadata()
except InvalidMetadataError as e:
print(f"Metadata validation failed: {e}")
### Follow-up Exercise
1. Modify your implementation to include logging at various levels (INFO, DEBUG, ERROR) for all major operations within the `Recipe` classes.
2. Introduce unit tests for each new method you have added using a testing framework such as `unittest` or `pytest`. Ensure that all edge cases are covered.
### Solution
python
import logging
# Configure logging at the beginning of your script/module.
logging.basicConfig(level=logging.DEBUG)
class Recipe:
...
class Package(recipe.RecipePackage):
...
class SourceFactory(factory.SourceFactory):
...
# Unit tests using unittest framework
import unittest
class TestRecipe(unittest.TestCase):
...
if __name__ == '__main__':
unittest.main()
This exercise challenges students to extend an already complex system by adding dynamic attribute handling, dependency management, advanced metadata operations, custom exceptions handling, and multi-source management—all while ensuring thread safety and efficiency.
[0]: from flask import Flask,jsonify,request
app=Flask(__name__)
data=[
{
"id":1,
"product":"Laptop",
"price":45000
},
{
"id":2,
"product":"Mouse",
"price":1500
},
{
"id":3,
"product":"Keyboard",
"price":2000
}
]
@app.route('/products',methods=['GET'])
def get_all_products():
return jsonify(data)
@app.route('/products/',methods=['GET'])
def get_product_by_id(id):
for product in data:
if product["id"]==id:
return jsonify(product)
return jsonify({"error":"Product