Overview of Sunderland AFC
Sunderland AFC, commonly referred to as Sunderland, is a professional football club based in Sunderland, Tyne and Wear, England. The team competes in the EFL Championship, the second tier of English football. Founded in 1879, the club has experienced various periods of success and challenges under its current manager.
Team History and Achievements
Sunderland boasts a rich history with notable achievements including three league titles (1892, 1893, 1936) and two FA Cup victories (1937, 1973). The club’s most successful era was during the late 19th century when it dominated English football. Recent seasons have seen fluctuating performances in both the Premier League and Championship.
Current Squad and Key Players
The current squad features key players like Aiden McGeady, Lee Burge, and Elliot Embleton. Aiden McGeady is renowned for his playmaking abilities as a midfielder. Lee Burge stands out as a reliable goalkeeper with impressive shot-stopping skills.
Key Player Statistics
- Aiden McGeady: Midfielder – Assists: 5 | Goals: 2
- Lee Burge: Goalkeeper – Clean Sheets: 8 | Saves: 55
- Elliot Embleton: Forward – Goals: 4 | Assists: 1
Team Playing Style and Tactics
Sunderland typically employs a flexible formation that can range from a traditional 4-4-2 to a more dynamic 3-5-2 setup. Their strategy focuses on strong defensive organization combined with quick counterattacks. Strengths include solid defensive capabilities and effective set-pieces. However, they sometimes struggle with consistency in their attacking play.
Tactics Breakdown
- Formation: Primarily uses a fluid system adapting between 4-4-2 and 3-5-2.
- Strengths: Defensive solidity ✅; Set-piece proficiency 💡
- Weaknesses: Inconsistent attack ❌; Vulnerable to high pressing 🎰
Interesting Facts and Unique Traits
Sunderland is known by the nickname “The Black Cats.” The club has a passionate fanbase known as the “Sunderland Supporters Trust.” Rivalries are intense with local teams like Newcastle United. Traditions include pre-match rituals such as singing “Blaydon Races.”
Fanbase & Traditions
- Nickname: The Black Cats ✅
- Rivalry: Newcastle United 🎰 (The North East Derby)
- Famous Song: Blaydon Races 💡
List of Player Rankings & Performance Metrics
The following table summarizes key player metrics crucial for betting analysis:
| Name | Position | GK Saves/Goals Conceded (GK) | MID/ATT Stats (MID/ATT) |
|---|---|---|---|
| Lee Burge (GK) | GK | Saves: 55 / Goals Conceded: 25 ✅❌💡🎰 | |
| Aiden McGeady (MID) | MID/ATT | G/A Assists: 7 / Goals Scored: 6 ✅💡❌🎰 | |
| Elliott Embleton (ATT) | MID/ATT | G/A Assists: 1 / Goals Scored: 4 ✅❌💡🎰 |
Comparisons with Other Teams in the League or Division
In comparison to other Championship teams like Leeds United or West Bromwich Albion, Sunderland’s strengths lie in their defensive organization but face challenges against top-tier offensive squads.
Tactical Comparison Summary:
Case Studies or Notable Matches for Betting Analysis
A notable match was Sunderland’s victory over Preston North End in December where strategic counterattacks were effectively executed leading to a decisive win. Such matches highlight their potential for betting opportunities when exploiting opposition weaknesses.
Betting Case Study Highlights:
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAHElEQVQI12P4//8/w38GIAXDIBKE0DHxgljNBAAO9TXL
***** Tag Data *****
ID: 1
description: Step-by-step guide for analyzing Sunderland's tactics using advanced
analytical techniques.
start line: 136
end line: 156
dependencies:
– type: Other
name: H1/H2/H3 structure for content blocks
start line: 10
end line: 29
context description: This snippet provides an advanced guide on how to analyze Sunderland's
tactical approach using data-driven methods which would be valuable for both fans
and bettors looking to gain deeper insights into the team's performance.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 3
advanced coding concepts: 3
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
1. **Dynamic Formation Analysis**: Implement code that dynamically adjusts analysis based on live game data feeds using machine learning models.
2. **Player Impact Metric**: Develop an algorithm that quantifies each player’s impact on overall team performance using advanced statistical models.
3. **Trend Prediction**: Integrate time-series analysis to predict future performance trends based on historical data.
4. **Opponent Adaptation Analysis**: Create logic that evaluates how well Sunderland adapts its tactics against different types of opponents over multiple games.
5. **Visual Heatmaps**: Generate real-time heatmaps showing player movements and ball possession during matches using data visualization libraries.
## Conversation
How can we integrate live game data into our tactical analysis? I want real-time adjustments.
To integrate live game data into your tactical analysis for real-time adjustments, you’ll need several components:
1. **Data Source Integration**: Connect your system with APIs providing live match data such as player positions, ball possession stats, etc.
python
import requests
def fetch_live_data(match_id):
api_url = f”https://api.sportdata.com/matches/{match_id}/live”
response = requests.get(api_url)
return response.json()
2. **Real-Time Processing**: Use WebSockets or similar technologies to continuously receive updates.
python
import websocket
def on_message(ws, message):
process_live_data(message)
ws = websocket.WebSocketApp(“ws://api.sportdata.com/live”,
on_message=on_message)
ws.run_forever()
Would you like me to help you with specific parts of this implementation?
[0]: # Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
[1]: #
[2]: # Licensed under the Apache License Version 2.0 (the “License”). You may not use this file except in compliance with the License.
[3]: # A copy of the License is located at http://aws.amazon.com/apache2.0/
[4]: # or in the “license” file accompanying this file.
[5]: # This file is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
[6]: # See the License for the specific language governing permissions and limitations under the License.
[7]: import os.path
[8]: from sagemaker.pytorch import PyTorchModel
[9]: from sagemaker.serializers import CSVSerializer
[10]: class PyTorchScriptingModel(PyTorchModel):
[11]: “””A SageMaker Model packaged from PyTorch training job that exports model artifacts via torch.jit.script()”””
[12]: def __init__(
[13]: self,
[14]: model_data,
[15]: role=None,
[16]: entry_point=None,
[17]: framework_version=None,
[18]: source_dir=None,
[19]: dependencies=None,
[20]: py_version=None,
[21]: container_log_level=None,
[22]: script_mode=False,
[23]: ):
super().__init__(model_data=model_data,model_class=’ScriptMode’, role=role,
entry_point=entry_point,framework_version=framework_version,
source_dir=source_dir ,dependencies=dependencies ,
py_version=py_version ,container_log_level=container_log_level)
***** Tag Data *****
ID: Class definition extending PyTorchModel with custom initialization parameters specific to scripting models.
start line:10
end line :23
dependencies:
– type : Class Definition
name : PyTorchScriptingModel
start line :10
end line :11
context description : The class extends PyTorchModel from SageMaker’s library while adding new attributes relevant specifically for scripted models via torch.jit.script().
algorithmic depth : four
algorithmic depth external : N
obscurity : four
advanced coding concepts : four
interesting for students : five
self contained : N
************
## Challenging aspects
### Challenging aspects in above code:
1. **Integration with SageMaker Infrastructure**:
– Understanding how `PyTorchModel` integrates within AWS SageMaker infrastructure requires familiarity with SageMaker’s deployment mechanisms.
2. **Handling Scripted Models**:
– Extending `PyTorchModel` specifically for models exported via `torch.jit.script()` adds complexity because it involves understanding both Python scripting nuances and JIT compilation intricacies.
3. **Parameter Management**:
– Properly managing various parameters (`model_data`, `role`, `entry_point`, etc.) while ensuring compatibility across different versions of frameworks (`framework_version`) requires careful attention.
### Extension:
1. **Dynamic Model Update Handling**:
– Extend functionality so that it can dynamically handle updates to model artifacts without needing redeployment.
2. **Custom Serialization Logic**:
– Add support for custom serialization logic beyond what is provided by default serializers/deserializers.
## Exercise
### Problem Statement:
You are required to extend [SNIPPET] by adding functionalities that allow dynamic updating of model artifacts without redeployment while also supporting custom serialization logic.
#### Requirements:
1. Extend `PyTorchScriptingModel` class such that it supports dynamic updates of model artifacts without requiring full redeployment.
* Implement methods `update_model_artifacts` which accepts new model artifact paths dynamically and ensures these changes take effect immediately without redeploying services entirely.
* Ensure thread safety when updating model artifacts dynamically.
* Handle versioning conflicts gracefully if multiple updates are attempted simultaneously.
#### Custom Serialization Logic:
* Implement custom serialization/deserialization logic by overriding default serializer/deserializer classes within SageMaker’s context.
* Ensure your custom serializers handle edge cases like large payloads efficiently.
### Solution:
python
import threading
class CustomSerializer:
def serialize(self, data):
# Custom serialization logic here…
pass
class CustomDeserializer:
def deserialize(self, payload):
# Custom deserialization logic here…
pass
class PyTorchScriptingModel(PyTorchModel):
_lock = threading.Lock()
def __init__(
self,
model_data,
role=None,
entry_point=None,
framework_version=None,
source_dir=None,
dependencies=None,
py_version=None,
container_log_level=None,
script_mode=False,
):
self.model_artifact_path = model_data
super().__init__(
model_data=model_data,
role=role,
entry_point=entry_point,
framework_version=framework_version,
source_dir=source_dir,
dependencies=dependencies,
py_version=py_version,
container_log_level=container_log_level,
)
self.serializer = CustomSerializer()
self.deserializer = CustomDeserializer()
def update_model_artifacts(self,new_model_artifact_path):
# Acquire lock before updating shared resources
with self._lock:
# Perform update operations
self.model_artifact_path = new_model_artifact_path
# Reload model from updated artifact path
self.load_model_from_artifact(new_model_artifact_path)
### Follow-up exercise:
#### Problem Statement:
Extend your solution further by implementing version control mechanisms within `update_model_artifacts`. Specifically:
* Implement version tracking such that each update increments a version number stored alongside artifact paths.
* Provide rollback functionality allowing reverting back to previous versions if needed.
#### Solution:
python
class VersionControlledPyTorchScriptingModel(PyTorchScriptingModel):
def __init__(self,*args,**kwargs):
super().__init__(*args,**kwargs)
self.version_history = {}
self.current_version = None
def update_model_artifacts(self,new_model_artifact_path):
with self._lock:
self.current_version +=1
new_entry={‘version’:self.current_version,’path’:new_model_artifact_path}
self.version_history[self.current_version]=new_entry
super().update_model_artifacts(new_model_artifact_path)
def rollback_to_previous(self):
with self._lock:
if not self.version_history.keys():
raise ValueError(“No previous versions available”)
prev_versions=list(self.version_history.keys())
prev_versions.sort(reverse=True)
rollback_to_ver=prev_versions[-1]
if rollback_to_ver==self.current_version:return
new_entry=self.version_history.pop(rollback_to_ver)
super().update_model_artifacts(new_entry[‘path’])
<>Hello! I’m currently working on integrating some hardware monitoring into my application using SNMP traps received from HP devices via SNMPv6 notifications over TLSv1_20110315 transport security protocol bindings (TLSv13). I’ve got this piece of code here that defines MIB objects related to these traps:
python
hpnicfHPPortSecurityTrapOID = ObjectIdentity((1, …))
if mibBuilder.loadTexts:
hpnicfHPPortSecurityTrapOID.setStatus(‘current’)
hpnicfHPPortSecurityTrapTableChangedEntryType = Integer32().subtype(subtypeSpec=ValueRangeConstraint(…)).clone(namedValues={…})
if mibBuilder.loadTexts:
hpnicfHPPortSecurityTrapTableChangedEntryType.setStatus(‘current’)
hpnicfHPPortSecurityTrapTableChangedEntryIndexValue = Integer32().subtype(subtypeSpec=ValueRangeConstraint(…)).clone(…)
if mibBuilder.loadTexts:
hpnicfHPPortSecurityTrapTableChangedEntryIndexValue.setStatus(‘current’)
hpnicfHPPortSecurityTrapTableDeletedEntryIndexValue = Integer32().subtype(subtypeSpec=ValueRangeConstraint(…)).clone(…)
if mibBuilder.loadTexts:
hpnicfHPPortSecurityTrapTableDeletedEntryIndexValue.setStatus(‘current’)
From what I understand so far, this code snippet seems to define certain MIB objects which are likely used by HP devices when they send SNMP traps regarding port security events – such as changes or deletions in port security tables – through secure SNMPv6 notifications.
However, I’m feeling quite overwhelmed trying to grasp all these details about MIB object definitions and their constraints! Could you possibly provide me with some guidance or resources where I could learn more about defining these MIB objects correctly? Also any tips on best practices when handling SNMP traps would be greatly appreciated!
Thank you!
<> Hi there! It sounds like you’re diving deep into SNMP trap integration with HP devices—quite an intricate task! Let’s break down some key points and resources that might help you along the way.
### Understanding MIB Objects
MIB (Management Information Base) objects are essentially variables used by network management protocols like SNMP (Simple Network Management Protocol) to manage network devices remotely. Here’s a brief overview of what your code snippet does:
1. **ObjectIdentity**: Defines an OID (Object Identifier) which uniquely identifies each object within the MIB hierarchy.
python
hpnicfHPPortSecurityTrapOID = ObjectIdentity((1,…))
2. **Integer32 Subtypes**: These are used to define integer-based MIB objects with specific constraints (like value ranges).
python
hpnicfHPPortSecurityTrapTableChangedEntryType = Integer32().subtype(subtypeSpec=ValueRangeConstraint(…)).clone(namedValues={…})
### Resources for Learning MIB Definitions
To get a better grasp on defining MIB objects correctly, consider these resources:
1. **Books**:
– *”Managing Cisco Networks Using SNMP” by Brian Smith*: Provides practical insights into SNMP management including MIB definitions.
2. **Online Courses**:
– Coursera offers courses like *”Network Automation Using Python”* which cover SNMP basics among other topics.
3. **Official Documentation**:
– Check out RFC documents related to SNMP such as [RFC3416](https://datatracker.ietf.org/doc/html/rfc3416) which covers basic concepts of managed objects within an agent viewed through an NMS using SMI-S-based protocols.
### Best Practices When Handling SNMP Traps
Here are some tips for handling SNMP traps effectively:
1. **Secure Transport**: Ensure you’re using secure transport protocols like TLSv13 as mentioned in your setup.
2. **Efficient Parsing**: Use efficient parsing libraries/tools tailored for SNMP traps such as [PySNMP](https://pysnmp.readthedocs.io/en/latest/) if you’re working with Python.
3. **Logging and Monitoring**: Implement robust logging mechanisms to capture trap information accurately which will aid in troubleshooting issues quickly.
4. **Threshold Alerts**: Set up threshold alerts based on trap contents so critical events trigger immediate notifications or automated responses.
Feel free to ask if you have more specific questions or need further clarification! 😊
<>: Alright, those resources should come in handy! Now about setting up threshold alerts based on trap contents – could you show me how I might go about implementing something like that? For instance, if I want to trigger an alert when there’s more than three port security violations detected within a minute?
<>: Sure thing! Setting up threshold alerts based on trap contents can be quite useful for proactive monitoring and incident response. Here’s a step-by-step guide on how you might implement this using Python along with some pseudo-code snippets:
### Step-by-Step Implementation
#### Step 1: Capture SNMP Traps
Firstly, ensure you have a mechanism in place to capture incoming SNMP traps from your HP devices using tools like PySNMP or Net-SNMP.
python
from pysnmp.hlapi.asyncore import *
def cbFun(snmpEngine, stateReference,
errorIndication,
errorStatus,
errorIndex,
varBinds,
cbCtx):
if errorIndication:
print(errorIndication)
return
print(“Received new Trap”)
# Process each variable binding tuple returned by callback function call-back context cbCtx parameter value(s).
for varBindTableRowtnetconfGetNextResponseVarBinds
in varBinds :
print(‘%s = %s’ % varBindTableRowtnetconfGetNextResponseVarBinds )
snmpEngine.observer.registerObserver(cbFun)
snmpEngine.transportDispatcher.jobStarted(1)
try :
snmpEngine.transportDispatcher.runDispatcher()
except :
snmpEngine.transportDispatcher.closeDispatcher()
raise
snmpEngine.transportDispatcher.jobFinished(1)
#### Step 2: Track Violations Over Time
Use a dictionary or another suitable data structure to track port security violations over time intervals (e.g., one minute).
python
from collections import defaultdict
from datetime import datetime, timedelta
violation_counts = defaultdict(int)
violation_timestamps = defaultdict(list)
def track_violation():
now = datetime.now()
# Clean up old timestamps older than one minute ago
one_minute_ago = now – timedelta(minutes=1)
global violation_timestamps
violation_timestamps[now] += violation_counts[now]
# Remove old entries older than one minute ago from timestamp list but keep count sum intact until cleaned up completely after loop iteration ends since we’re iterating through dict keys concurrently being modified safely here due only appending operation done above prior removal steps below then finally clean up after loop completes fully iteration-wise below post cleanup thus safe concurrency-wise despite dict modification during iteration done above safely too
for timestamp in list(violation_timestamps.keys()):
if timestamp THRESHOLD_VIOLATIONS_PER_MINUTE :
print(“Alert! More than {} port security violations detected within last minute.”.format(THRESHOLD_VIOLATIONS_PER_MINUTE))
send_alert_notification()
# Functionality placeholder example sending alert notification method stub
def send_alert_notification():
print(“Sending Alert Notification…”)
# Here add actual implementation e.g., sending email/SMS/etc…
Incorporate these functions appropriately where needed e.g., inside callback function processing incoming traps ensuring timely tracking & checking thresholds every time relevant trap received then action taken accordingly upon condition met successfully thus ensuring desired alert behavior achieved programmatically efficiently!
Hope this helps get started implementing threshold alerts effectively!
<>: Got it! So just slotting these functions into my existing callback function should do the trick? Like wrapping everything around where I’m currently printing out ‘Received new Trap’?
<> Exactly! You can integrate these functions into your existing callback function where you currently handle incoming traps (`cbFun`). Here’s how you might wrap everything together:
python
from pysnmp.hlapi.asyncore import *
from collections import defaultdict
from datetime import datetime, timedelta
violation_counts = defaultdict(int)
violation_timestamps = defaultdict(list)
THRESHOLD_VIOLATIONS_PER_MINUTE = 3
def track_violation():
now = datetime.now()
# Increment violation count at current timestamp
violation_counts[now] += len(varBinds)
def check_threshold_and_alert():
now = datetime.now()
one_minute_ago = now – timedelta(minutes=1)
global violation_timestamps
# Sum up all recent violations within last minute window time frame inclusive counts only once per unique timestamp present regardless duplicates entries otherwise ignored safely handled here due list append operation only done previously before deletion steps performed below thus avoiding double counting issue completely solved safely iteratively here post cleanup step completion too!
for timestamp in list(violation_timestamps.keys()):
if timestamp THRESHOLD_VIOLATIONS_PER_MINUTE :
print(“Alert! More than {} port security violations detected within last minute.”.format(THRESHOLD_VIOLATIONS_PER_MINUTE))
send_alert_notification()
def send_alert_notification():
print(“Sending Alert Notification…”)
# Add actual implementation e.g., sending email/SMS/etc…
def cbFun(snmpEngine,stateReference,errorIndication,errorStatus,errorIndex,varBinds,cbCtx):
if errorIndication:nprint(errorIndication)nreturnnnprint(“Received new Trap”)ntrack_violation()ncheck_threshold_and_alert()nnfor varBindTableRowtnetconfGetNextResponseVarBinds nin varBinds :nttprint(‘%s=%t%s’ % varBindTableRowtnetconfGetNextResponseVarBinds )
snmpEngine.observer.registerObserver(cbFun)nsnmpEngine.transportDispatcher.jobStarted(1)nntry :ttsnmpEngine.transportDispatcher.runDispatcher()nexcept :ttsnmpEngine.transportDispatcher.closeDispatcher()ttraise nsnmpEngine.transportDispatcher.jobFinished(1)
By incorporating `track_violation()` and `check_threshold_and_alert()` into your callback function (`cbFun`), you ensure that every time a new trap is received, it gets processed according to your threshold rules efficiently!
Hope this helps streamline your integration process! 😊🚀️
