Overview / Introduction about the Team
The Roanne Basket team hails from Roanne, France, and competes in the French LNB Pro B league. Founded in 1941, the team has developed a rich history within French basketball circles. Currently, under the management of coach Jean-Marc Dupraz, Roanne aims to climb the ranks in one of Europe’s competitive leagues.
Team History and Achievements
Roanne Basket has experienced several notable seasons throughout its history. The team reached the pinnacle of French basketball by winning the LNB Pro A title in 2017. Additionally, they have been regular contenders for promotion back to the top tier. Their performance has seen them finish as high as 4th place in past seasons.
Current Squad and Key Players
The current squad features key players such as Andrew Albicy, known for his playmaking abilities, and Alexis Ajinça, a dominant presence in the paint. Other standout players include Darius Adams, who brings scoring prowess to the team.
Team Playing Style and Tactics
Roanne employs a balanced offensive strategy focusing on ball movement and perimeter shooting. Defensively, they rely on strong interior defense led by their big men while maintaining active perimeter pressure. However, they sometimes struggle with consistency against teams with superior depth.
Interesting Facts and Unique Traits
Roanne is affectionately nicknamed “Les Verts,” reflecting their green color theme. The team boasts a passionate fanbase known for their enthusiastic support during games. They have a historic rivalry with Chalon-sur-Saône, making matchups between these teams highly anticipated events.
Lists & Rankings of Players, Stats, or Performance Metrics
- ✅ Andrew Albicy – Playmaking Leader (Assists)
- ❌ Defensive Challenges – Perimeter Defense Needs Improvement
- 🎰 Alexis Ajinça – Rebounding Powerhouse (Rebounds)
- 💡 Darius Adams – Scoring Threat (Points per Game)
Comparisons with Other Teams in the League or Division
Roanne is often compared to other mid-tier teams like Le Portel and Nancy due to similar aspirations of league promotion. While they may not match Le Mans’ depth or Gravelines’ defensive acumen, Roanne’s balanced approach offers competitiveness across various matchups.
Case Studies or Notable Matches
In their breakthrough season of 2016-2017, Roanne secured critical victories that propelled them into LNB Pro A contention. One standout game was their win against Bourg-en-Bresse where strategic adjustments led to a decisive victory.
Table Summarizing Team Stats and Recent Form
| Stat Category | Last Season Avg. | This Season Avg. |
|---|---|---|
| Pts/Game | 80.5 | 82.3 |
| Rebounds/Game | 37.8 | 39.1 |
| Ast/Game | 16.4 | 17.9 |
| H-F Record Last Season: | ||
| H Wins/Losses: | 15/11 | |
| A Wins/Losses: | 10/14 | |
Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡
To make informed betting decisions on Roanne Basket:
- Analyze head-to-head records against upcoming opponents to identify potential advantages.
- Closely monitor player injuries or lineup changes that could impact performance.
- Evaluate recent form trends over multiple games rather than isolated results.
- Favor bets when facing lower-ranked teams where Roanne’s balanced tactics can exploit weaknesses.
- Bet on over/under points when facing defensively weaker teams.
Bet on Roanne now at Betwhale!Quotes or Expert Opinions about the Team 🗣️ Quote Block 🗣️
“Roanne’s adaptability on both ends of the court makes them unpredictable opponents,” says sports analyst Jean-Luc Martin.
Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists ✅❌
- ✅ Strong Home Court Advantage: High morale boosts performance at home games.
- ❌ Inconsistency in Away Games: Struggles often arise when playing away from home due to travel fatigue and lack of fan support.
- ✅ Rising Star Talent: Young players are contributing significantly to both offense and defense.
- ❌ Depth Issues: Lack of depth compared to top-tier teams can be problematic during long seasons.</li
#include “ofApp.h”//————————————————————–
void ofApp::setup(){
ofSetVerticalSync(true);
ofSetFrameRate(60);// set up our camera
cam.setDistance(3000);
cam.setNearClip(1);
cam.setFov(90);// setup our light source
light.setPosition(ofGetWidth() * .5f,
ofGetHeight() * .5f,
1000);
light.enable();
light.setDiffuseColor(ofFloatColor(.8));
light.setSpecularColor(ofFloatColor(.8));// load our mesh
mesh.load(“jellyfish.obj”);// create some geometry from primitives
sphereMesh = new ofMesh();
sphereMesh->addSphere(50);planeMesh = new ofMesh();
planeMesh->addPlane(1000.f, 1000.f);cubeMesh = new ofMesh();
cubeMesh->addCube(100.f);// setup our shaders
shader.load(“shader.vert”, “shader.frag”);
shader.begin();
shader.setUniform1i(“tex”, 0);
shader.end();tex.load(“jellyfish.jpg”);
tex.bind();}
//————————————————————–
void ofApp::update(){}
//————————————————————–
void ofApp::draw(){
ofBackgroundGradient(ofColor::black);ofEnableDepthTest();
cam.begin();
shader.begin();
ofPushMatrix();
ofTranslate(-500,-500,-500);
sphereMesh->drawWireframe();
ofPopMatrix();shader.end();
glPolygonMode(GL_FRONT_AND_BACK,GL_LINE);
mesh.drawFaces();
glPolygonMode(GL_FRONT_AND_BACK,GL_FILL);
shader.begin();
ofPushMatrix();
ofTranslate(500,-500,-500);
sphereMesh->drawWireframe();
ofPopMatrix();shader.end();
glPolygonMode(GL_FRONT_AND_BACK,GL_FILL);
light.enable();
light.setPosition(cam.getPosition());
mesh.drawFaces();glDisable(GL_LIGHTING);
glEnable(GL_DEPTH_TEST);
}
//————————————————————–
void ofApp::keyPressed(int key){}
//————————————————————–
void ofApp::keyReleased(int key){}
//————————————————————–
void ofApp::mouseMoved(int x, int y ){}
//————————————————————–
void ofApp::mouseDragged(int x, int y, int button){}
//————————————————————–
void ofApp::mousePressed(int x, int y, int button){}
//————————————————————–
void ofApp::mouseReleased(int x, int y, int button){}
//————————————————————–
void ofApp::windowResized(int w, int h){}
christopherdaniels/processing-experiments<|file_sep[](https://github.com/josephmarkcoleman/openFrameworks/actions?query=workflow%3A"Continuous%20Integration")
[](https://github.com/josephmarkcoleman/openFrameworks/releases/latest)
[]()
[]()# openFrameworks
This repository contains my personal forked version of openFrameworks.
## Installation
Please see [openframeworks.org/download](http://openframeworks.cc/download/) for installation instructions.
## License
This project is licensed under MIT license.
## Contributing
Contributions are welcome! Please submit pull requests via GitHub.
## Credits
The original code is owned by openFrameworks developers:
– [arturoc](http://arturocastro.me/)
– [benjaminbannier](https://github.com/benjaminbannier)
– [damianblanco](http://www.damianblanco.com/)
– [danieltaz](http://www.danieltaz.com/)
– [dariomura](http://www.dariomura.com/)
– [david-bailey](http://davebailey.org/)
– [danomatika](http://danomatika.com/)
– [dominic-dietrich](http://www.dominicdietrich.de/)
– [drozdzynski](http://drozdzynski.info/)
– [efgeorgeoffreywatts]
(http://efgeorgeoffreywatts.co.uk/)
– [evan-williams]
(http://evanwilliams.github.io/)
– [federico-guido]
(http://federicoguido.it/en)
– [filipeocampo]
(http://filipeocampo.org/)
– [gaborboros]
(http://gaborboros.me)
– [gustavofrazao]
(http:/gustavofrazao.github.io)
– [kylemcdonald]
(http:/kylemcdonald.net)
– [luisfelipemorales]
(http:/luisfelipemorales.github.io)
– [michael-gruenstaeudl]
(http:/gruenstaeudl.at/michael/grafik-programming-in-cpp-and-openframeworks-for-the-impatient.html)
– [@nachtimwald]([https:/twitter.com/nachtimwald])
– [@niklas-hambrecht]([https:/twitter.com/niklas_hambrecht])
christopherdaniels/processing-experiments<|file_sep**This repository contains my personal forked version of Processing**Please see https://processing.org/download/ for installation instructions.
You can also download it directly from here:
* Windows Installer (.exe): https://github.com/josephmarkcoleman/processing/releases/download/v4r5/processing-windows64-v4r5.zip
* Mac OS X DMG (.zip): https://github.com/josephmarkcoleman/processing/releases/download/v4r5/processing-macosx64-v4r5.zip# Processing
Processing is an open-source graphical library and integrated development environment (IDE) built for electronic arts designers,
artists,
and visual designers with an emphasis on simplicity and ease-of-use.
It is intended primarily as a teaching tool,
but also used by professionals
for prototyping,
formal analysis,
and production.## Installation
Please see http:///download/
## License
This project is licensed under MIT license.
## Contributing
Contributions are welcome! Please submit pull requests via GitHub.
## Credits
The original code is owned by Processing developers:
* Ben Fry ([benfry]((http:/benfry.net)))
* Casey Reas ([reas]((http:/reas.com)))
* Daniel Shiffman ([shiffman]((http:/shiffman.net)))
* Joseph Wallace ([jwallace]((http:/jwallace.me)))The Processing core code was originally developed by Casey Reas,
Ben Fry,
Andrei Herasimchuk,
Andrew Madsen,
Daniel Rozin,
and Jonathan Feinberg.
adityachawla1999/GA-scheduling-algorithm<|file_sep|RFID|
— | —
Author | Aditya Chowdhary |
Email | [email protected] |
Date | March 23rd ,2021 |# RFID Based Automated Scheduling System Using Genetic Algorithm
### Project Description:
In this project I have implemented an automated scheduling system using genetic algorithm which will schedule classes according to students’ preferences without any conflicts between two classes i.e no student should attend two classes at same time slot.### Project Structure:
RFID/
├── README.md <- The top-level README file.
├── data <- Training data directory containing files downloaded from school server
│ ├── processed <- Processed data ready for modeling.
│ └── raw <- Data collected from school server in raw form.
├── notebooks <- Jupyter notebooks containing exploratory data analysis done before modeling process starts.
├── references <- Any publications cited.
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module so functions can be imported easily
├── data <- Scripts used to gather data from school server
│ └── make_dataset.py
├── feature_engineering <- Scripts used for data cleaning/preparation tasks are kept here
│ └── clean_data.py
├── models =3′
Numpy == ‘>=1’
Pandas == ‘>=1’
Jupyter Notebook == ‘>=6’
Matplotlib == ‘>=3’
Scipy == ‘>=1’### How To Run This Project:
To run this project just follow these steps:Step #01 : Clone this repository using git clone command :
git clone https:\github\com\adityachawla1999\GA-scheduling-algorithm.git
Step #02 : Go inside cloned folder using terminal command :
cd GA-scheduling-algorithm
Step #03 : Create virtual environment using following command :
python -m venv env
Step #04 : Activate virtual environment using following command :
source env/bin/activate
Step #05 : Install all required dependencies using following command :
pip install –upgrade pip
pip install –upgrade setuptoolspip install numpy==1.* pandas==1.* jupyter==6.* matplotlib==3.* scipy==1.*
pip install -r requirements.txt
Step #06 : Open jupyter notebook using following command :
jupyter notebook
Step #07 : Open `src/models/train_model.py` file inside jupyter notebook then run it cell by cell.
### Author Information:
Aditya Chowdhary
University Of Petroleum And Energy Studies
Dehradun
India
Email : [email protected] adityachawla1999/GA-scheduling-algorithm<|file_sep Commerce Seminar Assignment
========================================================
Aditya ChowdharyApril 12 ,2021
# Introduction
In this assignment we will be dealing with scheduling problem which will require us
to schedule classes according to students’ preferences without any conflicts between
two classes i.e no student should attend two classes at same time slot.We will solve this problem using genetic algorithm which will help us find optimal
solution efficiently.# Data Collection:
For solving this problem we need two things:
Firstly we need list of all students along with their class preferences.
Secondly we need list of all available classrooms along with their timings.
So first we’ll write script which will fetch information regarding students’ class
preferences from school server.Script Used For Fetching Student Preferences From School Server :
{r}
import numpy as np
import pandas as pd
import re
from datetime import datetimedef get_student_preferences():
student_data = pd.read_csv('student_data.csv')
student_preferences = []try:
print("nFetching Student Preferences …")
print("———————————-n")for index,row in student_data.iterrows():
if row['Class'] != 'B.Sc(Hons.) Computer Science':
continueif row['Roll No.'] 90:
continueif row[‘Roll No.’] %10 ==0:
continuestudent_id = row[‘Student ID’]
url = f”https://{row[‘Student ID’]}.edu.iitb.ac.in/django/rfid_app/get_preferences/”
page_text = get_page(url)
regex_pattern=r”(?<=
).*(?=
)”
extracted_string=re.search(regex_pattern,str(page_text)).group()
extracted_string=re.sub(‘<[^’, ”, extracted_string)
extracted_string=re.sub(r’n’, ‘,’, extracted_string)
extracted_list=re.split(‘,’,extracted_string)
extracted_list.pop()
cleaned_list=[]
for item in extracted_list:
item=item.strip()
cleaned_list.append(item)***** Tag Data *****
ID: 6
description: Main loop processing each student’s preferences fetched from URLs constructed
dynamically based on roll numbers; involves web scraping techniques combined with
regex operations for extracting relevant data from HTML content retrieved from URLs.
start line: 35
end line: 71
dependencies:
– type: Function
name: get_page(url)
start line: 24
end line: 34
context description: This snippet processes each student’s preferences obtained through
dynamic URL construction based on roll numbers fetched earlier; combines web scraping,
regex extraction techniques within iterative loops over DataFrame rows.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: ESNIPPET_REMOVED_INSTRUCTIONAL_PURPOSES************
## Challenging AspectsThe provided code snippet presents several challenging aspects that require careful consideration:
### Algorithmic Depth & Logical Complexity:
1. **Dynamic URL Construction**: Constructing URLs dynamically based on specific conditions adds complexity because it requires understanding how different roll number ranges affect URL generation.
2. **Data Filtering**: The filtering logic based on roll numbers (e.g., excluding certain ranges) requires precise implementation so that only valid entries are processed further.
3. **Web Scraping**: Extracting content via HTTP requests introduces challenges related to network reliability and handling different response formats (HTML/XML).
4. **Regex Extraction**: Using regular expressions (regex) effectively requires understanding complex patterns and ensuring robustness against malformed HTML structures.
5. **Data Cleaning**: Post-processing scraped content involves stripping unnecessary characters and splitting strings into lists while ensuring no critical information is lost or incorrectly parsed.
6. **Iterative Processing**: Iterating over DataFrame rows efficiently while maintaining performance can be tricky especially when dealing with large datasets.
### Extensions:
To extend these complexities further specifically tailored to this context:
1. **Handling Dynamic Content Updates**: Introduce scenarios where new rows might be added dynamically during processing requiring real-time updates or re-processing mechanisms.
2. **Advanced Filtering Logic**: Add more sophisticated filtering criteria involving multiple columns simultaneously (e.g., cross-referencing class types with specific subjects).
3. **Error Handling Enhancements**: Implement robust error handling mechanisms that deal not only with network errors but also parsing errors due to unexpected HTML structures or missing fields.
4. **Concurrency Management**: Incorporate concurrent processing strategies specific to handling multiple web scraping tasks simultaneously while avoiding race conditions unique to shared resources like network connections or temporary files used during parsing stages.
—
## Exercise
Your task is twofold:
Firstly you’ll expand upon an existing piece of code provided below ([SNIPPET]) by implementing additional functionalities specified below it.
Secondly you’ll enhance its robustness by addressing various edge cases mentioned subsequently.### Part A – Expand Functionality:
#### Requirements:
Expand upon `[SNIPPET]` such that it includes additional functionalities listed below:**New Functionalities**:
1) Implement functionality that handles dynamic updates wherein new rows might be added dynamically during processing requiring re-processing mechanisms.* If a new entry matching criteria (`Class`, `Roll No.`) appears after initial processing starts but before completion—process it too without restarting everything from scratch.
**Advanced Filtering Logic**:
Add advanced filtering logic involving cross-referencing multiple columns simultaneously e.g., filter out rows where `Class` type does not match specific subject criteria alongside existing conditions.**Error Handling Enhancements**:
Enhance error handling mechanisms including but not limited to network errors parsing errors due unexpected HTML structures missing fields etc.**Concurrency Management**:
Incorporate concurrency management strategies specific handling multiple web scraping tasks simultaneously avoiding race conditions shared resources like network connections temporary files parsing stages.#### Code Snippet Reference `[SNIPPET]`:
python
for index,row in student_data.iterrows():
if row[‘Class’] != ‘B.Sc(Hons.) Computer Science’:
continue
if row[‘Roll No.’] 90:
continue
if row[‘Roll No.’] %10 ==0:
continuestudent_id = row[‘Student ID’]
url = f”https://{row[‘Student ID’]}.edu.iitb.ac.in/django/rfid_app/get_preferences/”
page_text = get_page(url)
regex_pattern=r”(?<=
).*(?=
)”
extracted_string=re.search(regex_pattern,str(page_text)).group()
extracted_string=re.sub(‘<[^’, ”, extracted_string)
extracted_string=re.sub(r’n’, ‘,’, extracted_string)
extracted_list=re.split(‘,’,extracted_string)
extracted_list.pop()
cleaned_list=[]
for item in extracted_list:
item=item.strip()
cleaned_list.append(item)print(cleaned_list)
***** Tag Data *****
ID: 7
description: Complex regex operations followed by text cleaning steps transforming
HTML scraped content into structured list format suitable for further processing;
start line: 55
end line:70;
dependencies:
– type: Function Name Reference within main snippet context –
get_page(url)
context description:’Contextual relevance within main loop fetching preference details;
handles detailed text extraction/cleaning transformations necessary prior constructing/storing preference lists.’
algorithmic depth level:-EVALUATION_PENDING-
obscurity:-EVALUATION_PENDING-
advanced coding concepts:-EVALUATION_PENDING-
interesting-for-students:-EVALUATION_PENDING-
self contained:N/A*************
## Suggestions for complexity1. **Dynamic Regex Pattern Matching:** Modify the regex pattern dynamically based on certain conditions derived from `row`. This would involve generating different patterns depending on specific attributes found within each `row`.
2. **Asynchronous Requests:** Transform `get_page(url)` into an asynchronous function call using libraries like `asyncio` and `aiohttp` instead of synchronous requests while ensuring proper error handling remains intact.
3. **Contextual Text Extraction:** Instead of extracting text solely between `
` tags universally across all pages fetched via URLs derived from `row`, introduce logic that adjusts extraction rules based on metadata found within each page itself (e.g., different tags depending on page structure).
4. **Advanced Error Handling:** Implement more granular exception handling where specific types of exceptions trigger custom recovery actions instead of generic retry logic—perhaps logging detailed diagnostic information before retrying or aborting gracefully depending on error type severity levels detected during runtime analysis.
5. **Machine Learning Integration:** Integrate a pre-trained machine learning model that automatically determines whether certain parts of `page_text` should be included/excluded based upon learned patterns beyond simple regex matching—such as identifying non-standardized text blocks needing special treatment before being appended into `cleaned_list`.
## Conversation
[SNIPPET]
`Got it! This piece extracts text between `
` tags from HTML pages fetched via URLs derived from DataFrame rows.` What do you want me do?
`Let's start:` How about enhancing error handling so we handle different exceptions differently?
`Here's how we might do it:`python
try:
page_text = get_page(url)
except urllib.error.HTTPError as e_http_error:
if e_http_error.code == URLError.errno.ECONNREFUSED.errno():
print(f'HTTP Error {e_http_error.code} encountered at {url}')
elif e_http_error.code >=400 & e_http_error.code <=499:
print(f'Redirect Error {e_http_error.code} encountered at {url}')
else :
print(f'Server Error {e_http_error.code} encountered at {url}')
except urllib.error.URLError as e_url_error :
print(e_url_error.reason)
except Exception as e_generic_err :
print(f'Unexpected error occurred:n{str(e_generic_err)}')
raise e_generic_err
else:
`Continue with your existing logic here.`
