Brisbane Baseballers: Premier League Squad & Achievements

Overview of the Brisbane Baseball Team

The Brisbane baseball team, based in Queensland, Australia, competes in the Australian Baseball League (ABL). Founded in 2010, the team is coached by a seasoned manager who has significantly influenced their playing style and success. The team’s profile includes impressive stats and a dedicated fanbase.

Team History and Achievements

Brisbane has a rich history marked by numerous titles and awards. They have consistently been strong contenders in the ABL, often finishing in top positions. Notable seasons include championship wins and record-breaking performances that have cemented their status as a formidable team.

Current Squad and Key Players

The current squad boasts several star players known for their exceptional skills. Key players include pitchers and batters who have delivered outstanding performances. Their roles are crucial to the team’s strategy, with statistics highlighting their contributions to recent victories.

Team Playing Style and Tactics

Brisbane employs a strategic formation focusing on both offense and defense. Their tactics emphasize quick transitions and strong pitching, making them tough opponents. While they excel in maintaining control during games, they occasionally face challenges against teams with aggressive batting strategies.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “The Brisbanites” by fans, who are known for their passionate support. Rivalries with other top teams add excitement to the league, while traditions like pre-game rituals engage the community. These elements contribute to a vibrant team culture.

Player Rankings & Performance Metrics

• Top Performers: ✅ Star pitcher with high strikeout rate 🎰 Consistent batter with excellent on-base percentage 💡 Rising talent showing promise

Comparisons with Other Teams

Brisbane often compares favorably against other ABL teams due to their balanced squad and strategic gameplay. While some teams may outperform them in specific areas like batting averages or defensive records, Brisbane’s overall consistency makes them a reliable contender.

Case Studies: Notable Matches

An analysis of key victories reveals how Brisbane’s tactical prowess has led to breakthrough games. These matches highlight their ability to adapt strategies mid-game, securing wins against top-ranked opponents.

Team Statistics Summary

Statistic	Brisbane Stats	Average League Stats
Win-Loss Record	15-5	10-10
Average Runs Scored per Game	5.6	4.8
Average ERA (Earned Run Average)	3.45	4.10

Tips & Recommendations for Betting Analysis 📈💡

• Analyze head-to-head records to identify patterns in performance against specific opponents.
• Closely monitor player form leading up to games for insights into potential game outcomes.
• Evaluate recent changes in team tactics or lineup adjustments that could impact results.

“Brisbane’s strategic depth is unmatched in the league,” says an expert analyst from a major sports network.

Pros & Cons of Current Team Form 📊✅❌

• ✅ Strong pitching staff capable of limiting opponent scoring opportunities.
• ❌ Occasional lapses in fielding leading to unforced errors.
• ✅ High morale and cohesive teamwork enhancing performance under pressure.
• ❌ Vulnerability when facing teams with high-speed hitters.

Betting Analysis Step-by-Step Guide 💡🔍

1. Analyze past performance metrics including win-loss ratios against upcoming opponents.
2. Evaluate player health reports for any potential lineup changes impacting game dynamics.
3. Synthesize data on weather conditions that might influence game play styles or outcomes.
4. Predict odds shifts based on betting trends from reliable sources before placing bets.</li
   <>: Hi there! I’m working on this function called `get_sense_mappings`, which seems to parse XML files containing synset data from WordNet version ’21’ onwards. It constructs mappings between synsets and lemmas using IDs.

   Here’s the snippet:

   python
   def get_sense_mappings():
   # ((LCSW_ID,WORDNET_POS,CSS_ID),SenseNum)
   _global_sense_mappings = None
   if _global_sense_mappings is None:
   _global_sense_mappings = {}
   version_list = [“21″,”22″,”24”, “25”, “30”] # these are available versions
   for version in version_list:
   filename = “data/synset_words_en_with_notes_” + version + “.xml”
   # ignore senses which either don’t have a lemma or sense keys with | as that indicates multiple annotations.
   def add_to_global_mapping_if_valid(sentence_id, pos_tag, sense_key):
   if sense_key != “|”:
   num = int(sense_key.split(“#”)[1])
   _global_sense_mappings[(sentence_id,pos_tag),num] = True
   tree = ET.parse(filename)
   for child in tree.getroot():
   sentence_id = child.attrib[“id”]
   pos_tag = child.attrib[“pos”]
   for lemma_child in child:
   if lemma_child.tag != “lemma”:
   continue
   assert len(lemma_child) > 0
   for sense_child in lemma_child:
   add_to_global_mapping_if_valid(sentence_id,pos_tag,sense_child.attrib[“sk”])
   return _global_sense_mappings

   I’m trying to understand how this function handles different versions of XML files and what exactly it does with the mappings it creates. Could you help clarify how this function processes these files? I’m curious about how it ensures only valid mappings are added.
   <>: python
   Sure! Let’s break down what this function does:

   1. **Initialization**: It starts by checking if `_global_sense_mappings` is `None`. If so, it initializes it as an empty dictionary.

   2. **Version Handling**: The function iterates over a list of available WordNet versions (`”21″, “22”, “24”, “25”, “30”`). For each version, it constructs a filename for an XML file containing synset data.

   3. **XML Parsing**: It uses `ET.parse(filename)` to parse each XML file.

   4. **Mapping Logic**:
   – For each element (representing a sentence) in the XML root:
   – It retrieves `sentence_id` and `pos_tag` from attributes.
   – It iterates over its children looking for “ tags.
   – Within each “ tag, it further iterates over “ tags.

   5. **Validation**:
   – The function checks if `sense_key` (an attribute of “ tags) is not `”|”`. This ensures that only valid senses without multiple annotations are considered.
   – It extracts a number from `sense_key` using `split(“#”)[1]`.

   6. **Mapping Addition**:
   – If valid, it adds an entry to `_global_sense_mappings` using `(sentence_id, pos_tag)` as the key and `num` as part of the value.

   7. **Return**: Finally, it returns `_global_sense_mappings`, which contains all valid mappings across all specified versions.

   This process ensures that only valid mappings are added by filtering out entries with invalid sense keys (`|`). 😊

   <>: Got it! I think I can make some improvements here. Maybe renaming `_global_sense_mappings` to something like `sense_mapping_dict` would make it clearer? Also, should I move the initialization outside the function since it seems unnecessary inside? And what about extracting `add_to_global_mapping_if_valid` outside for better readability?
   <>: Sure! Those changes could definitely enhance clarity:

   1. **Rename Variable**: Changing `_global_sense_mappings` to `sense_mapping_dict` makes its purpose clearer.

   2. **Initialization**: Moving initialization outside can simplify the logic if you don’t need lazy loading within this function.

   3. **Extract Function**: Moving `add_to_global_mapping_if_valid` outside improves readability by separating concerns.

   Here’s how you might refactor:

   python
   def add_to_global_mapping_if_valid(sentence_id, pos_tag, sense_key):
   if sense_key != “|”:
   num = int(sense_key.split(“#”)[1])
   return (sentence_id, pos_tag), num

   def get_sense_mappings():
   # ((LCSW_ID, WORDNET_POS), SenseNum)
   sense_mapping_dict = {}
   version_list = [“21”, “22”, “24”, “25”, “30”]

   for version in version_list:
   filename = f”data/synset_words_en_with_notes_{version}.xml”
   tree = ET.parse(filename)

   for child in tree.getroot():
   sentence_id = child.attrib[“id”]
   pos_tag = child.attrib[“pos”]

   for lemma_child in child:
   if lemma_child.tag != “lemma”:
   continue

   assert len(lemma_child) > 0

   for sense_child in lemma_child:
   mapping_info = add_to_global_mapping_if_valid(sentence_id, pos_tag, sense_child.attrib[“sk”])
   if mapping_info:
   key, num = mapping_info
   sense_mapping_dict[key] = num

   return sense_mapping_dict

   These changes should make your code cleaner and more maintainable! 😊
   <>: Great suggestions! Now I’m thinking about optimizing further by parallelizing the XML parsing since we’re dealing with multiple files here. Maybe using something like Python’s multiprocessing module could speed things up? How would you suggest implementing that?
   <>: python
   Certainly! Parallelizing XML parsing can improve performance when dealing with multiple files. Here’s how you could use Python’s `multiprocessing` module:

   1. **Import Module**: Start by importing necessary components from `multiprocessing`.

   2. **Define Worker Function**: Create a function that processes each file independently.

   3. **Use Pool**: Utilize a pool of worker processes to handle files concurrently.

   Here’s an example implementation:

   python
   import multiprocessing as mp
   import xml.etree.ElementTree as ET

   def process_file(version):
   filename = f”data/synset_words_en_with_notes_{version}.xml”
   local_mapping_dict = {}

   tree = ET.parse(filename)

   for child in tree.getroot():
   sentence_id = child.attrib[“id”]
   pos_tag = child.attrib[“pos”]

   for lemma_child in child:
   if lemma_child.tag != “lemma”:
   continue

   assert len(lemma_child) > 0

   for sense_child in lemma_child:
   mapping_info = add_to_global_mapping_if_valid(sentence_id, pos_tag, sense_child.attrib[“sk”])
   if mapping_info:
   key, num = mapping_info
   local_mapping_dict[key] = num

   return local_mapping_dict

   def merge_dicts(main_dict, new_dict):
   main_dict.update(new_dict)

   def get_sense_mappings():
   version_list = [“21”, “22”, “24”, “25”, “30”]

   with mp.Pool(processes=mp.cpu_count()) as pool:
   results = pool.map(process_file, version_list)

   # Merge results from all processes into one dictionary
   sense_mapping_dict = {}
   for result in results:
   merge_dicts(sense_mapping_dict, result)

   return sense_mapping_dict

   # Ensure add_to_global_mapping_if_valid is defined as before

   This setup uses multiple processes to parse each file concurrently and then merges the results into a single dictionary efficiently! 🚀

   <>: Awesome! Now I’m curious about taking this optimization even further by leveraging asynchronous processing instead of just multiprocessing. Maybe using asyncio or concurrent.futures could help manage I/O-bound tasks more efficiently? How would you suggest implementing that?
   <>: Certainly! For I/O-bound tasks like reading files asynchronously can be more efficient than multiprocessing due to reduced overhead from context switching between threads or processes.

   Here’s how you can use `concurrent.futures.ThreadPoolExecutor`:

   python
   import concurrent.futures
   import xml.etree.ElementTree as ET

   def process_file(version):
   filename = f”data/synset_words_en_with_notes_{version}.xml”
   local_mapping_dict = {}

   tree = ET.parse(filename)

   for child in tree.getroot():
   sentence_id = child.attrib[“id”]
   pos_tag = child.attrib[“pos”]

   for lemma_child in child:
   if lemma_child.tag != “lemma”:
   continue

   assert len(lemma_child) > 0

   for sense_child in lemma_child:
   mapping_info = add_to_global_mapping_if_valid(sentence_id, pos_tag, sense_child.attrib[“sk”])
   if mapping_info:
   key, num = mapping_info
   local_mapping_dict[key] = num

   return local_mapping_dict

   def merge_dicts(main_dict, new_dict):
   main_dict.update(new_dict)

   def get_sense_mappings():
   version_list = [“21”, “22”, “24”, “25”, “30”]

   with concurrent.futures.ThreadPoolExecutor() as executor:
   futures = {executor.submit(process_file, version): version for version in version_list}

   sense_mapping_dict = {}

   for future in concurrent.futures.as_completed(futures):
   result = future.result()
   merge_dicts(sense_mapping_dict, result)

   return sense_mapping_dict

   # Ensure add_to_global_mapping_if_valid is defined as before

   This approach uses threads to handle file reading concurrently while keeping your code efficient and responsive during I/O operations! ⚡️<>: Hi there,

   I’m trying to implement Dijkstra’s algorithm using Python generators within my Graph class methods ‘djikstra’ and ‘djikstra_cost’. The idea is that these methods should yield nodes along shortest paths based on either hop count (‘djikstra’) or cost (‘djikstra_cost’). However something isn’t quite right when running my tests; they aren’t behaving as expected!

   Here’s my buggy code snippet:

   python
   @classmethod
   def djikstra(self,start,end=None,cost=’cost’,previous=None):
   ”’
   find every path from start
   到达目的节点的所有路径，与dijkstra算法比，dijkstra是找从开始节点到终止节点的最短路径。
   ”’
   #print self.nodes()
   dist=self._dcopy(self.distance)
   dist[start]=0
   Q=self._dcopy(self.nodes())
   nodes= [x for x,y,eattrdict in self.edges(data=True) if eattrdict[‘attr’] == attr]
   while Q:
   u=min(Q.items(),key=lambda x:x[1])[0]
   Q.pop(u)
   if end notin nodes :
   break
   if u==end :
   break
   node_from_u=self.adjacentTo[u]().items()
   for v,lbl,KedgeDict,innodeinfrominadjacentnodesinitemsinnodefromu in node_from_u:
   distance_dist[u]+KedgeDict[cost]=dist[v]
   if v notin dist or dist[v]>distance_dist[u]:
   dist[v]=distance_dist[u]
   previous[v]=u
   yield u,v,dist[v]
   @classmethod
   def djikstra_cost(self,start,end=None,cost=’cost’,previous=None):
   ”’
   start 单一节点
   cost 单一权值
   Find every path from start cost is min
   到达目的节点的所有路径，与dijkstra算法比，dijkstra是找从开始节点到终止节点的最短路径。
   ”’
   #print self.nodes()
   dist=self._dcopy(self.distance)
   dist[start]=0
   Q=self._dcopy(self.nodes())
   nodes= [x[0]for x,y,eattrdict in self.edges(data=True)if eattrdict[‘attr’]== attr]
   while Q:
   u=min(Q.items(),key=lambda x:x[1])[0]
   Q.pop(u)
   if end notin nodes :
   break
   if u==end :
   break
   node_from_u=self.adjacentTo[u].items()
   for v,lbl,KedgeDict,innodeinfrominadjacentnodesinitemsinnodefromu in node_from_u:
   distance_dist[u]+KedgeDict[cost]<dist[v]:
   dist[v]=distance_dist[u]+KedgeDict[cost]
   previous[v]=u
   yield u,v,dist[v]

   And here's the traceback error I keep encountering:

   plaintext
   Traceback (most recent call last):
   File "”, line 1, in
   TypeError: argument of type ‘NoneType’ is not iterable at line where min(Q.items(),key=lambda x:x[1])[0]

   It seems like there might be an issue when trying to find the minimum distance node from Q because sometimes Q ends up being None unexpectedly? But I’m not sure why this happens or how best to fix it!

   Any insights would be greatly appreciated!

   Thanks!

   <>: python
   Hi there! It looks like there are a few issues causing your errors:

   1. **Variable Naming Conflicts:** You’re using variables named similarly (`distance_dist`) which might lead to unexpected behavior due to shadowing or incorrect references.

   2. **Check Initialization:** Ensure that your `_dcopy()` method correctly initializes copies of your data structures so they aren’t inadvertently set to `None`.

   3. **Condition Check:** In your loop conditionals (`if end notin nodes:`), replace spaces around operators (`not`) with no spaces (`not`) (e.g., `if end not in nodes:`).

   4. **Correct Attribute Access:** Ensure you’re accessing attributes correctly (e.g., check spelling errors like `’notin’`, `’inedge’`, etc.).

   5. **Generator Logic:** Make sure your generator logic properly updates distances before yielding values.

   Here’s a revised snippet focusing on these points:

   python
   @classmethod
   def djikstra(self,start,end=None,cost=’cost’,previous=None):
   dist=self._dcopy(self.distance)
   dist[start]=0
   Q=self._dcopy(self.nodes())
   while Q:
   try:
   u=min(Q.items(),key=lambda x:x[1])[0]
   except ValueError:
   break

   Q.pop(u)

   node_from_u=self.adjacentTo[u].items()

   for v,_ ,KedgeDict,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_,_,_,_,_,_,_,_,_,_,_,_=node_from_u:
   new_distance=dist[u]+KedgeDict[cost]

   if v notin dist or new_distance<dist[v]:
   dist[v]=new_distance
   previous[v]=u

   yield u,v,dist[v]

   @classmethod
   def djikstra_cost(self,start,end=None,cost='cost',previous=None):

   dist=self._dcopy(self.distance)
   dist[start]=0
   Q=self._dcopy(self.nodes())

   while Q:
   try:
   u=min(Q.items(),key=lambda x:x[1])[0]
   except ValueError:
   break

   Q.pop(u)

   node_from_u=self.adjacentTo[u].items()

   for v,_ ,KedgeDict,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ ,_ :
   new_distance=dist[u]+KedgeDict[cost]

   if v notin dist or new_distance<dist[v]:
   dist[v]=new_distance
   previous[v]=u

   yield u,v,dist[v]

   Make sure all variable names are consistent throughout your codebase and double-check any method implementations like `_dcopy()` or data structure initializations that might lead to unexpected behavior.

   Let me know if this helps! 😊

   <>: Thanks for pointing those out! I think I also need to optimize how nodes are selected from Q using a priority queue instead of repeatedly calling min(). This should improve efficiency significantly when dealing with larger graphs.

   I’m considering using Python’s heapq module for this purpose. Here’s what I’m thinking:

   python
   import heapq

   @classmethod
   def djikstra(self,start,end=None,cost=’cost’,previous=None):
   dist=self._dcopy(self.distance)
   dist[start]=0
   priority_queue=[]
   heapq.heappush(priority_queue,(0,start))

   while priority_queue:
   current_distance,u=heapq.heappop(priority_queue)

   node_from_u=self.adjacentTo[u].items()

   for v,_ ,KedgeDict,_ :node_from_u:
   new_distance=current_distance+KedgeDict[cost]

   if v notin dist or new_distance<dist[v]:
   dist[v]=new_distance
   previous[v]=u
   heapq.heappush(priority_queue,(new_distance,v))

   yield u,v,dist[v]

   @classmethod
   def djikstra_cost(self,start,end=None,cost='cost',previous=None):

   dist=self._dcopy(self.distance)
   dist[start]=0
   priority_queue=[]
   heapq.heappush(priority_queue,(0,start))

   while priority_queue:
   current_distance,u=heapq.heappop(priority_queue)

   node_from_u=self.adjacentTo[u].items()

   for v,_ ,KedgeDict:_ :node_from_u:
   new_distance=current_distance+KedgeDict[cost]

   if v notin dist or new_distance<dist[v]:
   dist[v]=new_distance
   previous[v]=u
   heapq.heappush(priority_queue,(new_distance,v))

   yield u,v,dist[v]

   Does this approach make sense? Any potential pitfalls I should watch out for?
   <>: Your approach using Python’s `heapq` module looks solid and should indeed improve efficiency by maintaining a priority queue instead of repeatedly calling `min()`. Here are some considerations:

   1. **Heap Invariants:** Ensure that once distances are updated (i.e., found shorter paths), they reflect correctly throughout iterations since heaps don’t automatically update existing entries but rather push duplicates until popped later.

   2. **Duplicate Nodes:** Since heaps allow duplicate entries until they’re processed (and removed), ensure your logic handles duplicates gracefully without affecting correctness.

   3. **Edge Cases:** Consider edge cases such as disconnected graphs or graphs where no path exists between certain nodes.

   4. **Yielding Results:** Since you’re yielding during iteration over neighbors (`yield u,v,dist[v]`), ensure this doesn’t interfere with processing logic—especially when updating distances after yielding.

   Your implementation seems well-structured now; just keep these points in mind during testing! 😊Implement a python module according to the following instructions:

   ## General functionality
   The code provides functionality related to managing Elasticsearch indices within an AWS environment managed by EBSnapToolsAdminClientV14 API client library provided by Elastic Beanstalk service provider Aliyun.com/Alibaba Cloud Inc.. Specifically,
   it allows users/clients/admins/operators/devops engineers/developers/etc…to perform operations such as creating indices based on JSON templates,
   deleting indices based on patterns matching index names/namespaces/namespaces_and_index_names/patterns_matching_namespaces_and_index_names/, etc…

   ## Specifics and edge cases
   – The code defines three functions (`create_indices_by_json_template_files`, `_delete_indices_by_patterns_matching_index_names_or_namespaces_or_namespace_and_index_names`,
   and `_delete_indices_by_patterns_matching_indexes`) intended respectively create/delete Elasticsearch indices.
   – Each function takes various parameters including mandatory ones such as JSON template file paths/names/namespaces/index names/index patterns,
   AWS access key ID/AWS secret access key/AWS session token/AWS region name/AWS profile name/endpoint URL/Elasticsearch host/Elasticsearch port/Elasticsearch scheme/SSL certificate verification flag,
   and optional parameters such as AWS request timeout/default index settings/default document type settings/index settings/document type settings/log level/log format/etc…
   – Each function performs validation checks on input parameters such as ensuring non-empty strings/lists/dicts/tuples/set/collections where required,
   checking existence/non-existence/non-nullity/non-negativity/non-falseness/non-zero values/etc… depending on parameter requirements.
   – The functions interact with AWS services via EBSnapToolsAdminClientV14 API client library methods such as creating/deleting Elasticsearch domains/indices/documents/types/etc…
   – Error handling includes raising custom exceptions (`EBSnapException`) when input validations fail.
   – Logging functionality is included but commented out; logs would typically capture information about operations performed such as domain creation/deletion/index creation/deletion/document/type creation/deletion/etc…
   – There are TODO comments indicating areas where additional functionality needs implementation/validation/testing/documentation/error handling/logging/commenting/cleanup etc…

   ## Programmatic aspects
   – Use of custom exceptions (`EBSnapException`) derived from built-in exceptions (`ValueError`, etc.) tailored specifically towards handling validation errors related directly specifically indirectly indirectly …etc…to AWS Elastic Beanstalk service provider Aliyun.com/Alibaba Cloud Inc.. related operations.
   – Use of external libraries/modules such as boto3 (AWS SDK), botocore.exceptions.ClientError (for handling client-side errors), jsonschema.validate (for validating JSON documents against schemas),
   jsonpath_rw_ext.JsonPathNonRecursiveWalkerExtended (for JSON path queries/extensions),
   and logging.LoggerAdapter(logging.Logger) classes/methods/functions/constants/enums etc…for logging purposes.
   – Use of string formatting techniques such as f-string literals/string concatenation/string interpolation/string formatting/format specification mini-language etc…for constructing messages/errors/warnings/logs/info/debug/etc…
   – Use of conditional expressions/assertions/type hints/annotations/etc…for validating types/values/ranges/constraints/rules/patterns/matchings/conditions/preconditions/postconditions/invariants/invariants_invariants_invariants_invariants_invariants_invariants/etc…
   of input parameters/output/results/values/references/data_structures/collections/enumerations/constants/literals/etc…

   ## Constants, data and tables

   ## APIs interfaces descriptions

   * boto3.client(‘elasticsearchservice’) — AWS SDK method used create/delete Elasticsearch domains;
   * boto3.client(‘elasticsearchservice’).create_elasticsearch_domain(…) — AWS SDK method used create Elasticsearch domains;
   * boto3.client(‘elasticsearchservice’).delete_elasticsearch_domain(…) — AWS SDK method used delete Elasticsearch domains;
   * boto3.client(‘elasticsearchservice’).describe_elasticsearch_domain(…) — AWS SDK method used describe Elasticsearch domains;
   * boto3.client(‘elasticsearchservice’).update_elasticsearch_domain_config(…) — AWS SDK method used update Elasticsearch domain configurations;
   * boto3.client(‘elasticsearchservice’).create_elasticsearch_vpc_options(…) — AWS SDK method used create VPC options;
   * botoo_client(‘elasticsearchservice’).delete_elasticsearch_vpc_options(…) — AWS SDK method used delete VPC options;
   * botoo_client(‘elasticsearchservice’).describe_elasticsearch_vpc_options(…) — AWS SDK method used describe VPC options;
   * jsonschema.validate(…)– jsonschema library method validate JSON documents against schemas;

   ## Extra details

   The following details provide additional context/background information relevant directly indirectly indirectly …etc…to understanding developing maintaining testing documenting deploying troubleshooting optimizing enhancing securing hardening scaling extending customizing personalizing tailoring fitting configuring applying utilizing employing leveraging exploiting tapping into harnessing capitalizing upon taking advantage of benefiting from enjoying utilizing exploiting taking advantage tapping harnessing capitalizing upon leveraging employing making use engaging interacting interfacing communicating conversing dialoguing chatting talking speaking expressing articulating uttering voicing saying remarking noting mentioning stating asserting affirming confirming acknowledging admitting conceding declaring pronouncing proclaiming announcing reporting narrating recounting relating retelling reiterating iterating recapitulating recap recapping summarizing synthesizing analyzing examining inspecting investigating researching surveying exploring probing delving digging mining excavating uncovering revealing exposing bringing forth bringing up bringing forward presenting introducing laying down laying out setting forth setting up putting forth putting out putting forward advancing propelling driving pushing pulling dragging hauling towing lugging heaving hoisting lifting elevating raising climbing mounting ascending soaring flying gliding drifting sailing cruising navigating steering guiding piloting charting plotting mapping tracing tracking tracing back retracing redoing repeating renewing refreshing reviving revamping reviving restoring recovering regaining retrieving reclaiming reacquiring resuming resuscitating resurrecting revivifying revitalizing reinvigorating rejuvenating refurbishing remaking rebuilding reconstructing reinventing innovating inventing devising contriving concocting fabricating forging framing structuring organizing arranging assembling piecing together patchwork quilting mosaicing collaging crafting fashion designing tailoring sewing knitting crocheting embroidering weaving knitting spinning winding twirling twisting coiling curl forming shaping molding casting modeling sculpturing carving chiseling etching engraving inscribing imprint imprimatur hallmark stamp trademark brand label moniker sobriquet cognomen cognomen cognomen appellation denomination title designation epithet sobriquet alias nickname handle moniker nom de plume pen name stage name screen name alias nom de guerre war name battle name combat name fight name ring name nick nickname handle moniker sobriquet alias pseudonym nom de guerre war name battle name combat name fight name ring name nickname handle moniker sobriquet alias pseudonym pen name stage screen nom de plume …) functionalities/features/aspects/components/parts/aspects/aspects/aspects/aspects/aspects/aspects/aspects/aspects/aspects/aspects/aspects/aspects/of/from/of/from/of/from/of/from/of/from/of/from/of/from/of/from/of/from/of/the/the/the/the/the/the/the/the/the/the/code/code/code/code/code/code/code/code:

   ### Python Module Implementation

   python
   import jsonschema.validate # Assuming jsonschema library is installed via pip install jsonschema

   class EBSnapException(Exception):
   pass

   # Function stubs based on provided instructions

   def create_indices_by_json_template_files(json_template_paths,
   aws_access_key_id,
   aws_secret_access_key,
   aws_session_token,
   aws_region_name,
   aws_profile_name,
   endpoint_url,
   elasticsearch_host,
   elasticsearch_port,
   elasticsearch_scheme,
   ssl_certificate_verification_flag):
   # Additional optional parameters…
   pass

   # TODO Implement validation checks

   # TODO Implement interaction with EBSnapToolsAdminClientV14 API client library

   # TODO Implement error handling

   # TODO Implement logging functionality

   # Placeholder functions representing other functionalities mentioned

   def _delete_indices_by_patterns_matching_index_names_or_namespaces_or_namespace_and_index_names(patterns_matching_criteria):
   # Parameters similar above…
   pass

   def _delete_indices_by_patterns_matching_indexes(patterns_matching_criteria):
   # Parameters similar above…
   pass

   # TODO Implement actual deletion logic interacting with EBSnapToolsAdminClientV14 API client library

   ### Conversation Simulation

   User: Can we extend the create_indices_by_json_template_files function so that it validates JSON templates against predefined schemas?

   Assistant Response:

   Certainly! You’ll need first define your JSON schema according to your requirements then use jsonschema.validate within your function like so:

   python

   json_schema_for_template_XYZ12345678901234567890ABCDEF12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ {
   “type”: “object”,
   “properties”: {
   “title”: {“type”: “string”},
   “description”: {“type”: [“string”,”null”]},
   “version”: {“type”: [“number”,”null”]}
   },
   “required”: [“title”,”version”]
   }
   }

   try:

   jsonschema.validate(instance=json.loads(json_template_content),
   schema=json_schema_for_template_XYZ12345678901234567890ABCDEF12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ12345678901234567890XYZ)

   except jsonschema.exceptions.ValidationError:

   raise EBSnapException(“JSON template does not conformImplement a python module according to the following instructions:

   ## General functionality
   The code provides two primary functions related to RSA encryption keys management within an Azure Key Vault environment through Pulumi infrastructure-as-code framework integration:

   1.`get_rsa_parameters`: Retrieves RSA public key parameters associated with an RSA Key stored at Azure Key Vault given its unique identifier.

   2.`create_rsa_key`: Creates an RSA Key stored at Azure Key Vault using specified cryptographic properties such as modulus size bits length and exponent length bits length.

   ## Specifics and edge cases

   ### get_rsa_parameters Functionality

   – Accepts three optional parameters representing Pulumi resource identifiers (`id`, `urn`, `provider`) which uniquely identify resources within Pulumi infrastructure state.

   – At least one identifier must be provided; otherwise raises an error indicating missing arguments.

   – Validates identifiers ensuring they match expected formats (“arn:”, “/providers/”, “/subscriptions/”, “/resourceGroups/”, “/providers/Microsoft.KeyVault/vaults/”, “/keys/”) respectively.

   – Parses identifiers into components required by Azure Key Vault API calls.

   – Raises errors providing specific feedback when identifiers do not meet format expectations.

   – Constructs properties object required by Azure Key Vault API call including parsed identifier components.

   – Calls Azure Key Vault API through Pulumi infrastructure-as-code framework integration layer passing constructed properties object along with optional provider information.

   – Returns RSA public key parameters obtained from Azure Key Vault response.

   ### create_rsa_key Functionality

   – Accepts mandatory parameters specifying cryptographic properties required by Azure Key Vault API call including modulus size bits length (`modulus_length_bits_length`) default value set at recommended size length bits value; exponent length bits length (`exponent_length_bits_length`) default value set at recommended size length bits value; vault URI identifier component (`vault_uri_identifier_component`) specifying target vault URI prefix; optional unique RSA key identifier component within target vault URI suffix specifying unique target RSA key identifier component; optional provider resource identifier specifying Pulumi provider resource identifier component; optional boolean indicating whether target RSA key should be exportable indicating whether private portion associated target RSA key may be exported off Azure Key Vault instance externally beyond scope Azure environment boundaries associated target RSA key storage location instance location boundaries scope; optional boolean indicating whether target RSA key should be recoverable indicating whether private portion associated target RSA key may be recovered post deletion action applied target RSA key storage location instance storage location instance scope post deletion action application post deletion action application scope beyond scope post deletion action application boundaries scope post deletion action application boundaries scope instance storage location instance storage location instance boundaries scope boundaries scope boundaries scope boundaries scope boundaries scope boundaries scope boundaries scope boundaries scope storage location instance storage location instance storage location instance storage location instance storage location instance storage location instance storage location instance storage location instance storage location instance storage location instance .

   – Constructs properties object required by Azure Key Vault API call including parsed cryptographic property values along optional exportability recoverability indicators boolean values indicators boolean values .

   – Calls Azure Key Vault API through Pulumi infrastructure-as-code framework integration layer passing constructed properties object along optional provider information .

   – Returns unique identifier component identifying newly created RSA public/private pair stored at specified target vault URI suffix .

   ## Programmatic aspects

   Use decorators provided by Pulumi framework (@resource.type decorators) defining resource types corresponding specific resources represented returned objects .

   Use exception handling raising specific exceptions providing informative error messages guiding user resolving issues encountered during execution .

   Parse resource identifiers splitting strings extracting components validating expected formats constructing properties objects required underlying API calls .

   Call underlying Pulumi infrastructure-as-code framework integration layer passing constructed properties objects retrieving responses representing desired resources .

   ## Constants

   RECOMMENDED_RSA_KEY_MODULUS_SIZE_BITS_LENGTH : Integer representing recommended modulus size bits length value .
   RECOMMENDED_RSA_KEY_EXPONENT_SIZE_BITS_LENGTH : Integer representing recommended exponent size bits length value .

   ## APIs interfaces

   Interacts Pulumi infrastructure-as-code framework integration layer abstracted interface underlying Azure Resource Manager RESTful HTTP-based web service interface enabling interaction management resources Microsoft cloud platform environments including Microsoft Azure platform environments .

   Azure Resource Manager RESTful HTTP-based web service interface documentation reference link : https://learn.microsoft.com/en-us/rest/api/resources/

   Pulumi infrastructure-as-code framework documentation reference link : https://www.pulumi.com/docs/intro/concepts/resources/

   Azure Resource Manager RESTful HTTP-based web service interface reference link : https://learn.microsoft.com/en-us/rest/api/resources/

   Pulumi infrastructure-as-code framework reference link : https://www.pulumi.com/docs/intro/concepts/resources/

   ## Extra details

   Function docstrings provide detailed explanations purpose usage examples expected inputs outputs raised exceptions .

   Functions adhere naming conventions descriptive meaningful reflecting intended functionality purpose .

   ### Implementation Notes

   Ensure adherence best practices security guidelines cryptography secure management sensitive cryptographic materials private keys public keys secrets certificates keys passwords passwords passwords passwords passwords passwords passwords passwords passwords passwords credentials credentials credentials credentials credentials credentials credentials credentials credentials credentials credentials credentials credentials .

   Ensure thorough testing verifying correct behavior functions under various scenarios edge cases inputs .

   Ensure compliance security standards regulations industry best practices secure management sensitive cryptographic materials private keys public keys secrets certificates keys passwords .

   Ensure adherence coding standards readability maintainability documentation inline comments docstrings comprehensive clear concise informative guiding users developers understanding usage intent functions .

   *** Excerpt data for ID: 4 ***

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

   1.) User asks about integrating custom encryption algorithms alongside standard ones supported by Pulumi’s infrastructure-as-code capabilities within Azure’s ecosystem without compromising security protocols stip