Negele Arsi vs Kedus Giorgis

Expert Opinion on Negele Arsi vs Kedus Giorgis

This football match between Negele Arsi and Kedus Giorgis is anticipated to be a tightly contested affair. The odds suggest a low-scoring game, with a prediction of under 2.5 goals at 63.80%. Both teams are known for their defensive strategies, which could lead to fewer scoring opportunities. Negele Arsi has been solid at home, while Kedus Giorgis has shown resilience in away matches. This match could hinge on tactical discipline and counter-attacks.

Betting List: Under 2.5 Goals

• Prediction: 63.80%
• Analysis: The likelihood of under 2.5 goals reflects the defensive nature of both teams. Expect a game focused on tactical play rather than high scoring.
• Key Factors:
  • Negele Arsi’s home advantage may contribute to their defensive stability.
  • Kedus Giorgis’ ability to perform well away from home suggests they might adopt a cautious approach.

Betting List: Negele Arsi Win

• Prediction: Not specified, but worth considering given their strong home performance.
• Analysis: Negele Arsi’s familiarity with the pitch and supportive crowd could give them an edge in securing a win.
• Key Factors:
  • Recent form and player availability could influence their performance.
  • The team’s ability to capitalize on set-pieces might be crucial.

Betting List: Kedus Giorgis Win

• Prediction: Not specified, but their resilience in away games makes them a contender.
• Analysis: Kedus Giorgis’ strategic play and experience in similar matches could lead to an upset.
• Key Factors:
  • Their defensive setup will be key in preventing goals and potentially scoring from counter-attacks.
  • Momentum from recent performances could boost their confidence.

Betting List: Draw

• Prediction: Implied by the under 2.5 goals market, making it a plausible outcome.
• Analysis: Given the defensive strengths of both teams, a draw seems likely, especially if neither side can break through the other’s defense.
• Key Factors:
  • The tactical battle between managers could lead to a stalemate.
  • Both teams’ focus on maintaining a clean sheet might result in limited scoring chances.

Betting List: Both Teams to Score (BTTS)

• Prediction: Less likely given the under 2.5 goals market but still possible with strategic plays.
• Analysis: While less probable, breakthroughs from either side could occur if one team manages to exploit defensive lapses.
• Key Factors:
  • The attacking players’ form and ability to capitalize on opportunities will be crucial.
  • Moments of individual brilliance or tactical errors could lead to goals from both sides.

Betting List: Over 1.5 Goals

• Prediction: Contrary to the under 2.5 goals market, but worth considering if either team takes more risks.
• Analysis: If either team shifts from a defensive stance to a more attacking approach, there could be more than one goal scored.
• Key Factors:
  • The pressure of the match situation might force teams to take more risks offensively.
  • Injuries or suspensions affecting key defenders could lead to more goals being conceded.

  </il[0]: #!/usr/bin/env python3

  [1]: # Copyright (c) Facebook, Inc. and its affiliates.
  [2]: # This source code is licensed under the MIT license found in the
  [3]: # LICENSE file in the root directory of this source tree.

  [4]: import json
  [5]: import logging
  [6]: import os
  [7]: import re
  [8]: import subprocess
  [9]: import sys
  [10]: import time

  [11]: import boto3

  [12]: from .util import get_ecs_task_arns_for_service

  [13]: def get_boto3_client(service_name=None):
  [14]: """
  [15]: Create boto3 client for AWS services.

  [16]: Args:
  [17]: service_name (str): name of service client.

  [18]: Returns:
  [19]: boto3.client object.
  [20]: """
  [21]: return boto3.client(service_name)

  [22]: def get_boto3_resource(service_name=None):
  [23]: """
  [24]: Create boto3 resource for AWS services.

  [25]: Args:
  [26]: service_name (str): name of service resource.

  [27]: Returns:
  [28]: boto3.resource object.
  [29]: """
  [30]: return boto3.resource(service_name)

  [31]: def get_ecr_images(
  [32]: registry_id=None,
  [33]: repository_name=None,
  [34]: ):
  [35]: """
  [36]: Get images from Amazon ECR.

  [37]: Args:
  [38]: registry_id (str): id of ECR registry.
  [39]: repository_name (str): name of repository.

  [40]: Returns:
  [41]: list of image objects.
  [42]: """
  [43]: if not registry_id:
  [44]: registry_id = os.environ["AWS_ACCOUNT_ID"]

  [45]: if not repository_name:
  [46]: repository_name = "criteo-retail-research"

  [47]: ecr_client = get_boto3_client("ecr")

  [48]: try:

  response = ecr_client.describe_images(
  registryId=registry_id,
  repositoryName=repository_name,
  )

  images = response["imageDetails"]

  except ecr_client.exceptions.RepositoryNotFoundException as err:

  logging.info(err)

  images = []

  return images

  [49]: def get_ecr_image_tags(
  [50]: image_digest=None,

  ):

  ecr_client = get_boto3_client("ecr")

  try:

  response = ecr_client.batch_get_image(
  registryId=os.environ["AWS_ACCOUNT_ID"],
  repositoryName="criteo-retail-research",
  imageIds=[
  {
  "imageDigest": image_digest,
  },
  ],
  )

  tags = response["images"][0]["imageManifest"]["tags"]

  except ecr_client.exceptions.RepositoryNotFoundException as err:

  logging.info(err)

  tags = []

  return tags

  ***** Tag Data *****
  ID: 4
  description: This snippet demonstrates advanced error handling and use of environment
  variables within AWS ECR interaction context.
  start line: 31
  end line: 48
  dependencies:
  – type: Function
  name: get_boto3_client
  start line: 13
  end line: 21
  context description: The function `get_ecr_images` fetches image details from Amazon
  ECR while handling exceptions like `RepositoryNotFoundException`. It also demonstrates
  fallbacks using environment variables.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: Y

  *************
  ## Suggestions for complexity

  1. **Caching Mechanism:** Implement a caching mechanism that stores previously fetched ECR image details locally to minimize API calls and improve performance.

  2. **Pagination Handling:** Modify the function to handle paginated responses from AWS ECR if there are many images in the repository.

  3. **Tag Filtering:** Add functionality that allows filtering images based on specific tags or other metadata criteria.

  4. **Parallel Requests:** Enable parallel requests to fetch details from multiple repositories concurrently using threading or async calls.

  5. **Custom Error Handling:** Implement custom error handling logic that logs specific details about why certain repositories or images couldn't be fetched, including retry mechanisms for transient errors.

  ## Conversation

  I need help with this code I have [SNIPPET] how can I make it cache results so I don’t call describe_images every time

  Sure! You can implement a simple caching mechanism using Python’s `functools.lru_cache`. Here’s how you can modify your function:

  python
  from functools import lru_cache

  @lru_cache(maxsize=32)
  def get_ecr_images(
  registry_id=None,
  repository_name=None,
  ):
  “””
  Get images from Amazon ECR.
  Args:
  registry_id (str): id of ECR registry.
  repository_name (str): name of repository.
  Returns:
  list of image objects.
  “””
  if not registry_id:
  registry_id = os.environ[“AWS_ACCOUNT_ID”]
  if not repository_name:
  repository_name = “criteo-retail-research”

  ecr_client = get_boto3_client(“ecr”)

  try:
  response = ecr_client.describe_images(
  registryId=registry_id,
  repositoryName=repository_name,
  )
  images = response[“imageDetails”]
  except ecr_client.exceptions.RepositoryNotFoundException as err:
  logging.info(err)
  images = []

  return images

  This uses `lru_cache` to cache up to the last `32` calls made to `get_ecr_images`. You can adjust the `maxsize` parameter as needed.

  what if there are many images i need pagination too

  To handle pagination, you can use the `nextToken` parameter provided by AWS SDKs for paginated results. Here’s how you can modify your function:

  python
  def get_ecr_images(
  registry_id=None,
  repository_name=None,
  ):
  “””
  Get images from Amazon ECR with pagination.
  Args:
  registry_id (str): id of ECR registry.
  repository_name (str): name of repository.
  Returns:
  list of image objects.
  “””
  if not registry_id:
  registry_id = os.environ[“AWS_ACCOUNT_ID”]
  if not repository_name:
  repository_name = “criteo-retail-research”

  ecr_client = get_boto3_client(“ecr”)

  images = []

  next_token = None

  while True:
  try:
  response = ecr_client.describe_images(
  registryId=registry_id,
  repositoryName=repository_name,
  nextToken=next_token,
  )
  images.extend(response.get(“imageDetails”, []))

  next_token = response.get(‘nextToken’)

  if not next_token:
  break

  except ecr_client.exceptions.RepositoryNotFoundException as err:
  logging.info(err)
  break

  return images

  This loop will continue fetching images until there are no more pages (`nextToken` is None).

  can we add tag filtering so i only get images with specific tags

  Certainly! You can add an optional parameter for tags and filter the results accordingly. Here’s how you can do it:

  python
  def get_ecr_images(
  registry_id=None,
  repository_name=None,
  filter_tags=None, # New parameter for tag filtering
  ):
  “””
  Get images from Amazon ECR with pagination and tag filtering.

  Args:
  registry_id (str): id of ECR registry.
  repository_name (str): name of repository.
  filter_tags (dict): dictionary of tags to filter by.

  Returns:
  list of image objects.
  “””

  if not registry_id:
  registry_id = os.environ[“AWS_ACCOUNT_ID”]

  if not repository_name:
  repository_name = “criteo-retail-research”

  ecr_client = get_boto3_client(“ecr”)

  all_images = []

  next_token = None

  while True:
  try:
  response = ecr_client.describe_images(
  registryId=registry_id,
  repositoryName=repository_name,
  nextToken=next_token,
  )
  all_images.extend(response.get(“imageDetails”, []))

  next_token = response.get(‘nextToken’)

  if not next_token:
  break

  except ecr_client.exceptions.RepositoryNotFoundException as err:
  logging.info(err)
  break

  if filter_tags is None or not filter_tags:
  return all_images

  filtered_images = []

  for image in all_images:
  tags_match = all(
  any(tag[‘key’] == k and tag[‘value’] == v for tag in image.get(‘imageTags’, []))
  for k, v in filter_tags.items()
  )

  if tags_match:
  filtered_images.append(image)

  return filtered_images

  With this modification, you can pass a dictionary `filter_tags` where keys are tag names and values are tag values you want to filter by.

  can we make it fetch multiple repositories concurrently using threading

  Sure! You can use Python’s `concurrent.futures.ThreadPoolExecutor` for making concurrent requests. Here’s how you can modify your function:

  python
  from concurrent.futures import ThreadPoolExecutor, as_completed

  def fetch_images_for_repository(ecr_client, registry_id, repository_name):
  all_images = []

  next_token = None

  while True:
  try:
  response = ecr_client.describe_images(
  registryId=registry_id,
  repositoryName=repository_name,
  nextToken=next_token,
  )
  all_images.extend(response.get(“imageDetails”, []))

  next_token = response.get(‘nextToken’)

  if not next_token:
  break

  except ecr_client.exceptions.RepositoryNotFoundException as err:
  logging.info(f”Repository {repository_name} not found.”)
  break

  return all_images

  def get_ecr_images_concurrently(
  repositories_list, # List of repositories names instead of single repo name
  max_workers=5 # Number of concurrent threads
  ):
  “””
  Get images from multiple Amazon ECR repositories concurrently with pagination.

  Args:
  repositories_list (list): list of repository names.
  max_workers (int): maximum number of threads for concurrent execution.

  Returns:
  dict containing lists of image objects per each repository.
  “””

  if not repositories_list:
  raise ValueError(“repositories_list cannot be empty”)

  ecr_client = get_boto3_client(“ecr”)

  results_dict = {}

  with ThreadPoolExecutor(max_workers=max_workers) as executor:
  future_to_repo = {
  executor.submit(fetch_images_for_repository, ecr_client, os.environ[“AWS_ACCOUNT_ID”], repo): repo
  for repo in repositories_list}

  for future in as_completed(future_to_repo):
  repo = future_to_repo[future]
  try:
  results_dict[repo] = future.result()
  except Exception as exc:
  logging.error(f”Repository {repo} generated an exception: {exc}”)
  results_dict[repo] = []

  return results_dict

  # Usage example
  repositories_list=[‘repo1’, ‘repo2’, ‘repo3’]
  images_by_repo=get_ecr_images_concurrently(repositories_list)

  This code uses `ThreadPoolExecutor` to fetch images concurrently from multiple repositories provided in `repositories_list`. The results are stored in a dictionary where keys are repository names and values are lists of image details.

  user

  I’m working on my final project at university which involves developing an Android app that allows users to view locations using Google Maps API v2. I’ve already created an activity that enables users to add locations using Google Places API v1 which are then saved into a database table called “locations”. These locations are subsequently displayed on Google Maps using Google Maps API v2.

  I have encountered some issues when trying to populate markers onto Google Maps when loading my activity at startup. Specifically, I am facing two main problems:

  1. I need assistance with writing a query that retrieves all location data from my database table named “locations”.

  This is my current query attempt which doesn’t seem correct:

  //THIS QUERY DOES NOT WORK!
  Cursor cursor;
  String[] projection;
  projection={Locations._ID,Locations.COLUMN_NAME_ADDRESS,Locations.COLUMN_NAME_LATITUDE,Locations.COLUMN_NAME_LONGITUDE};
  cursor=db.query(Locations.TABLE_NAME,null,null,null,null,null,null);
  //I've tried using projection here too but it didn't work either!
  

  In addition,

  2. I am unsure about how best to display these markers on Google Maps upon loading my activity at startup!

  This is what I’ve attempted so far but it doesn’t seem right!

  //THIS DOES NOT WORK EITHER!
  
  //googleMap.addMarker(new MarkerOptions().position(new LatLng(0d,0d)).title("Hello World"));
  
  //Instead I want it so that when my activity loads up it shows me all my locations that are saved into my database table called "locations".
  
  //For example; If I had saved London into my database table called "locations" then upon loading up my activity it would show me London on Google Maps!
  
  //I've been searching around google but haven't been able find any examples or documentation showing me how I should go about doing this!
  
  //Any help would be much appreciated!
  
  </