Utica Comets: A Comprehensive Guide for Sports Bettors

Overview of the Utica Comets

The Utica Comets are a professional ice hockey team based in Utica, New York. Competing in the American Hockey League (AHL), they were established in 1996. The team is known for its dynamic play and serves as a development squad for the NHL’s Toronto Maple Leafs.

Team History and Achievements

The Utica Comets have been a staple in the AHL since their inception. They have secured several division titles and have consistently been contenders for the Calder Cup. Notable seasons include their impressive performances in the early 2000s and their recent playoff appearances.

Current Squad and Key Players

The current roster features promising talents like Timothy Liljegren, a defenseman known for his defensive prowess, and Nick Robertson, a forward with exceptional scoring abilities. The team is led by head coach Chris Taylor.

Key Players:

• Nick Robertson: Forward – Known for his goal-scoring ability.
• Timothy Liljegren: Defenseman – Recognized for his defensive skills.

Team Playing Style and Tactics

The Comets employ an aggressive forechecking strategy combined with strong defensive coverage. Their strengths lie in their speed and physicality, while weaknesses may include occasional lapses in defensive coordination.

Tactics:

• Formation: 1-3-1 Forecheck
• Strengths: Speed, Physical Play
• Weaknesses: Defensive Coordination

Interesting Facts and Unique Traits

Fans affectionately call them “The Comets,” reflecting their fast-paced style of play. They have a passionate fanbase known as “The Comet Crew.” Rivalries with teams like the Rochester Americans add excitement to their games.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Nick Robertson – Top Scorer
• ❌ Defensive Lapses – Area for Improvement
• 🎰 Upcoming Match Odds – Analyze Trends
• 💡 Player Development – Watch Young Talents Shine

Comparisons with Other Teams in the League or Division

The Utica Comets often compare favorably against teams like the Hartford Wolf Pack due to their robust player development program. While both teams are competitive, the Comets’ connection to the Maple Leafs provides them with top-tier prospects.

Case Studies or Notable Matches

A memorable game was their 2021 playoff series against the Providence Bruins, where they showcased resilience and strategic depth. This series highlighted key players stepping up under pressure.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

Analyze player performance trends leading up to games to identify potential breakout stars. Consider betting on games where key players are healthy and rested.

Betting Tips:

• Analyze head-to-head records before placing bets.
• Favor games where star players are expected to perform well.
• Maintain awareness of injury reports impacting player availability.

Quotes or Expert Opinions About the Team

“The Utica Comets are not just a feeder team; they’re a powerhouse developing future NHL talent,” says hockey analyst John Doe.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

• ✅ Strong Offensive Lineup: Capable of high-scoring games.
• ❌ Inconsistent Defense: Can lead to unexpected losses against weaker teams.
• ✅ Young Talent: Offers potential for growth throughout the season.</li
• ❌ Injuries: Affect key player availability during critical matches.</l
  i

  Frequently Asked Questions about Betting on Utica Comets 🤔

  How does betting on AHL teams like Utica differ from NHL?

  Betting on AHL teams can be more unpredictable due to less media coverage and fewer betting markets compared to NHL games. However, it offers opportunities for higher returns if you understand team dynamics well.

  What should I consider when analyzing upcoming matches?

  Consider factors such as player injuries, recent performance trends, head-to-head statistics against opponents, and any changes in coaching strategies.

  Are there any specific odds or markets recommended for betting on AHL games? 10000:

  [15]: indices = np.argpartition(similarity_matrix,
  [16]: -num_top,
  [17]: axis=-1)[
  [18]: :, -num_top:]

  top_k_similarities.append(np.take_along_axis(
  similarity_matrix[i],
  indices[i],
  axis=-1))

  top_k_similarities.append(np.take_along_axis(
  similarity_matrix[:, i],
  indices[:, i],
  axis=-1))

  top_k_similarities[i].sort(axis=-1)[::-1]

  top_k_similarities[-i].sort(axis=0)[::-1]

  ***** Tag Data *****
  ID: 4
  description: Complex nested operations involving sorting along axes within loops.
  start line: 51
  end line: 65
  dependencies:
  – type: Function
  name: get_top_k_similarities
  start line: 7
  end line: 66
  context description: This snippet contains nested operations that sort arrays along
  different axes within loops which can be non-trivial to follow due to multiple levels
  of nesting.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: Y

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

  Here are five advanced ways that could be relevant to expand or modify logic:

  1. **Dynamic Top-K Calculation**: Modify `num_top` calculation dynamically based on certain conditions within each iteration instead of pre-computation.

  2. **Parallel Processing**: Implement parallel processing using Python’s multiprocessing module to handle large similarity matrices more efficiently.

  3. **Custom Sorting Criteria**: Allow custom sorting criteria based on user-defined functions instead of simple descending order.

  4. **Sparse Matrix Optimization**: Optimize handling large sparse matrices using libraries like SciPy’s sparse matrix functionalities.

  5. **Memory Management**: Integrate memory management techniques such as lazy evaluation or chunk processing when dealing with very large matrices.

  ## Conversation

  [SNIPPET] why do we need two separate sortings?

  `top_k_similarities[i]` sorts each row individually while `top_k_similarities[-i]` sorts columns individually because we want both row-wise and column-wise sorted values from `similarity_matrix`. Each sorting ensures we capture top similarities either across rows or down columns.

  I want custom sorting criteria not just descending how?

  `You can pass a custom comparison function using numpy’s argsort method.` Here’s how you can modify it:

  python
  def custom_sort(arr):
  return arr.argsort() + (-arr).argsort()

  for i in range(len(top_k_similarities)):

  indices_row = custom_sort(top_k_similarities[i])
  indices_col = custom_sort(top_k_similarities[-i])

  top_k_similarities[i] = np.take_along_axis(top_k_similarities[i], indices_row[::-1], axis=-1)
  top_k_similarities[-i] = np.take_along_axis(top_k_similarities[-i], indices_col[::-1], axis=0)

  This allows you to define your own sorting logic inside `custom_sort()`.

  i need dynamic num_top calculation inside loop possible?

  `Yes`, you can calculate `num_top` dynamically within each loop iteration based on some condition:

  python
  for i in range(num_samples):

  current_num_top = min(num_samples – i – 1,
  int(np.sqrt(num_samples)))

  indices_row = np.argpartition(similarity_matrix[i],
  -current_num_top)[-current_num_top:]

  indices_col = np.argpartition(similarity_matrix[:, i],
  -current_num_top)[:, -current_num_top:]

  top_k_row = np.take_along_axis(similarity_matrix[i], indices_row, axis=-1)
  top_k_col = np.take_along_axis(similarity_matrix[:, i], indices_col, axis=0)

  top_k_row.sort(axis=-1)[::-1]
  top_k_col.sort(axis=0)[::-1]

  if len(top_k_similarities) <= i:
  top_k_similarities.append([])

  if len(top_k_row) == current_num_top:
  top_k_similarities[i].append(top_k_row)

  if len(top_k_col) == current_num_top:
  top_k_similarities.append([top_k_col])

  This recalculates `num_top` dynamically during each iteration.

  i want parallel processing here suggestions?

  `You can use Python’s multiprocessing module to parallelize this process.` Here’s an example:

  python
  import multiprocessing as mp

  def process_chunk(chunk_index):
  chunk_result = []

  current_num_top = min(num_samples – chunk_index – 1,
  int(np.sqrt(num_samples)))

  indices_row = np.argpartition(similarity_matrix[chunk_index],
  -current_num_top)[-current_num_top:]

  indices_col = np.argpartition(similarity_matrix[:, chunk_index],
  -current_num_top)[:, -current_num_top:]

  top_k_row = np.take_along_axis(similarity_matrix[chunk_index],
  indices_row,
  axis=-1)

  top_k_col = np.take_along_axis(similarity_matrix[:, chunk_index],
  indices_col,
  axis=0)

  chunk_result.append((chunk_index,top_ksim))

  chunk_result.append((chunk_index,-top_ksim))

  chunk_result.append((chunk_index,top_ksim))

  chunk_result.append((chunk_index,-top_ksim))

  chunk_result.append((chunk_index,top_ksim.sort(axis=-1)[::-True]))

  chunk_result.append((chunk_index,-top_ksim.sort(axis=0)[::-True]))

  return chunk_result

  if __name__ == ‘__main__’:

  with mp.Pool(mp.cpu_count()) as pool:

  results = pool.map(process_chunk, range(num_samples))

  # Flatten results into single list.
  flattened_results = [item for sublist in results for item in sublist]

  # Process flattened_results accordingly…

  This uses multiprocessing to handle chunks of data concurrently.
  Remember that multiprocessing has overhead so it’s beneficial mainly when working with very large datasets.
  *** Excerpt ***

  *** Revision 0 ***

  ## Plan

  To create an exercise that is advanced and challenging:

  – The excerpt needs to contain complex sentence structures that require careful parsing.
  – It should include specialized terminology that assumes prior knowledge beyond what is given directly.
  – There should be references made implicitly requiring deductive reasoning—wherein one must infer information from what is presented indirectly.
  – Incorporate nested counterfactuals (hypothetical “what if” scenarios) and conditionals (if/then statements), making logical connections between various parts necessary.
  – It would be beneficial if the excerpt required understanding of interdisciplinary knowledge—linking concepts across different fields such as science, history, philosophy etc.

  ## Rewritten Excerpt

  In an alternate timeline where Mendel had integrated principles of quantum mechanics into his foundational work on genetics—postulating that genetic inheritance operates not only through discrete units but also through probabilistic quantum states—the trajectory of molecular biology might have diverged significantly from our historical record. Suppose further that Heisenberg had drawn inspiration from Mendelian inheritance patterns when formulating his uncertainty principle; then one could argue that our contemporary understanding of genetic determinism would embody inherent uncertainties analogous to those observed at subatomic scales.

  Imagine now that Darwin had access not only to Mendel’s hypothetical quantum-genetic framework but also possessed insights into continental drift—a theory he did not live long enough to see fully developed by Wegener—and synthesized these concepts into his theory of natural selection before Wallace independently proposed similar ideas without this additional layer of complexity.

  Had these scientific giants collaborated across disciplines earlier than historically recorded—with Mendel providing a probabilistic view on heredity informed by nascent quantum theories; Heisenberg considering biological implications alongside physical phenomena; Darwin integrating geological time scales into evolutionary theory—it stands to reason that our present-day paradigms across multiple scientific disciplines might exhibit fundamentally different axioms predicated upon these early interdisciplinary syntheses.

  ## Suggested Exercise

  Consider an alternative historical scenario where Gregor Mendel incorporated principles akin to quantum mechanics into his genetic research; Werner Heisenberg drew inspiration from Mendelian genetics when formulating his uncertainty principle; Charles Darwin had insights into both Mendelian quantum-genetics and Alfred Wegener’s continental drift theory before Arthur Wallace conceptualized natural selection independently.

  Given this counterfactual situation:

  What might be one plausible outcome regarding modern scientific paradigms?

  A) The uncertainty inherent in genetic expression would become a central tenet across all life sciences disciplines due directly to Heisenberg’s influence on biological thought processes.

  B) Molecular biology would likely disregard classical genetics entirely since Mendel’s hypothetical integration suggests genes operate solely within probabilistic frameworks rather than deterministic ones.

  C) Evolutionary biology would remain unaffected since Darwin’s original formulation without consideration of geological time scales was sufficient enough at its core principles.

  D) Interdisciplinary collaboration among scientists could potentially result in foundational theories uniting physics’ probabilistic nature with biological evolution patterns influenced by geology over extensive time periods.

  *** Revision 1 ***

  check requirements:
  – req_no: 1
  discussion: The draft does not require advanced external knowledge explicitly;
  it remains self-contained within hypothetical scenarios.
  score: 0
  – req_no: 2
  discussion: Understanding subtleties is needed but not clearly tied back to external,
  advanced knowledge outside what’s presented.
  score: 2
  – req_no: 3
  discussion: Excerpt length and complexity meet requirements; however, difficulty
  could be enhanced by incorporating specific scientific theories or facts.
  score: 2
  – req_no: 4
  discussion: Choices are misleading but could benefit from being grounded more firmly
  in real-world scientific principles or theories.
  score: 2
  – req_no: ‘5’
  discussion’: The exercise poses some challenge but lacks depth without requiring
  specific external academic knowledge.’
  revision suggestion”: “To satisfy requirement #1 better, integrate specific references
  tto real-world scientific theories such as SchrxF6dingeru2019s cat paradox
  tto illustrate quantum superposition applied hypothetically tto genetics within
  tthe excerpt itself.nnFor requirement #4 improvement,tthe choices should reflect
  tmore nuanced understanding by referencing actual scientific principles (e.g.,
  tmentioning epigenetics alongside classical genetics). This approach requires
  learnerstto understand both the hypothetical scenario presentedtand how it contrasts
  twith real-world science.nnTo enhance adherencento requirement #5,nconsidertmakingtthe
  tcorrect answer hinge upon understanding bothtthe specificstof quantum mechanics’
  correct choice: Interdisciplinary collaboration among scientists could potentially
  result in foundational theories uniting physics’ probabilistic nature with biological evolution patterns influenced by geology over extensive time periods,
  revised exercise’: “Given an alternative historical scenario described above where:n-
  Mendel integrates principles akin to quantum mechanics into his genetic research;n-
  Heisenberg draws inspiration from Mendelian genetics when formulating his uncertainty principle;n-
  Darwin possesses insights into Mendelian quantum-genetics and Wegener’s continental drift theory before Wallace conceptualizes natural selection independently.nnConsidering the hypothetical integration of SchrxF6dingeru2019s cat paradox concept to illustrate how genetic traits might exist in a state of superposition until observed,nwhich outcome regarding modern scientific paradigms is most plausible?”
  incorrect choices:
  – The uncertainty inherent in genetic expression would become a central tenet across all life sciences disciplines due directly to Heisenberg’s influence on biological thought processes.
  – Molecular biology would likely disregard classical genetics entirely since Mendel’s hypothetical integration suggests genes operate solely within probabilistic frameworks rather than deterministic ones.
  – Evolutionary biology would remain unaffected since Darwin’s original formulation without consideration of geological time scales was sufficient enough at it core principles.
  *** Revision 2 ***

  check requirements:
  – req_no: ‘1’
  discussion: Draft relies too heavily on hypothetical scenarios without requiring,
  explicitly or implicitly, knowledge outside those scenarios.
  score: ‘0’
  – req_no: ‘2’
  discussion’: Understanding subtleties is encouraged but not effectively tied back’
  ‘to necessitate external advanced knowledge.’
  score’: ’20’
  external fact’: Quantum entanglement effects observed experimentally contrasted with ‘
  ? hypothetical superpositions proposed in genetic contexts., ‘
  revision suggestion”: “To satisfy requirement #l better , integrate specific references”
  revised excerpt”: |-

  *** Revision ***
  To fulfill Requirement #l more effectively , incorporate direct comparisons between actual experimental observations related tQ quantum entanglement effects , contrasting them w/ hypothetical superpositions proposed herein regarding genetic contexts . This approach will compel learners t utilize their understanding o experimental physics alongside theoretical biology . For Requirement l#l , ensure subtle nuances mentioned require deep comprehension linked explicitly t real-world applications o similar theoretical constructs . To enhance adherence t Requirement l#l , base correct answer o precise understanding o both specifics o quantum mechanics And biologically relevant examples .
  correct choice : Integrating concepts o superposition w/ classical genetics could lead t novel interpretations o phenotypic variability influenced b environmental factors .
  revised exercise”: |

  Given th e alternative historical scenario described above wh er e :
  M end el integrates principles akin t o qu antum me chanics into h is gen et ic research ;
  H eise nber g draws ins piration fr om Men delian genes ts wh en formu lating h is un cer ta int y princ ip le ;
  D ar win possesses insights int o Men delian qu antum-g en et ic s framework s an d W eg ener’ s contine nt al dr iff th eory b ef ore W all ace c onsce ptualizes natu ral se le ction independently .

  C onsider ing th e hyp ot he tic integrat ion o f Schr dinger’ s cat parado x concept t o ill ustrate how gen et ic traits mi g ht ex ist i n a state o f su perpositio n until ob served ,

  Which outcome regard ing modern scie ntific paradigms is most plausib le ?
  incorrect choices:
  ‘ Gen et ic determinism wou ld become obsolete d ue t direct influen ce fr om qu antum ph ysics ‘
  Qu antum gen etics wou ld replace cl assical gen etics entirely redefining h eredita ry pr inciples b eside mutations alone ‘
  Disciplines w ithin life sciences wou ld unify under qu antum theoretical framewor ks disregarding traditional biological evidences ‘
  *** Revision 3 ***

  check requirements:
  – req_no: ‘1’
  discussion’: The draft does not effectively require external advanced knowledge beyond
  basic understanding.’
  score’: ’10’
  external fact’: Knowledge about Quantum Biology specifically relating entangled states’
  revision suggestion”: To meet Requirement #l more robustly , connect discussions about”
  revised excerpt”: |-

  *** Revision ***
  Revised Excerpt:
  {“MAIN_EXCERPT”: “Incorporating quantum mechanical principles such as superposition into classical genetic models presents intriguing possibilities including non-binary gene expressions influenced by environmental interactions resembling entangled states observed experimentally in other systems.”}

  Correct choice:
  Integrating concepts of superposition with classical genetics might offer new insights into phenotypic variability influenced by environmental factors.

  Revised exercise:
  Given the revised excerpt above discussing integration between classical genetics and quantum mechanical principles like superposition:

  Which outcome regarding modern scientific paradigms appears most plausible?
  incorrect choices:
  Genetic determinism becomes obsolete due directly from influences derived purely from quantum physics fundamentals alone.
  Quantum genetics completely replaces traditional genetic models focusing solely beyond mutation-based hereditary mechanisms alone.
  Disciplines within life sciences consolidate under unified quantum theoretical frameworks dismissing conventional biological evidence altogether.

  *** Revision 4 ***

  check requirements:
  – req_no: ‘1’
  discussion’: Needs deeper integration with specialized knowledge outside general familiarity.’
  score’: ’20’
  external fact’: Specific studies demonstrating non-binary gene expressions resulting from environmental interactions similar to entangled states found elsewhere scientifically.’
  revision suggestion”: To improve fulfillment of Requirement #l,# ensure deeper engagement with specialized topics such as Quantum Biology studies comparing gene expressions affected by environmental factors versus standard binary expressions seen traditionally.”
  revised excerpt”: “”Incorporating sophisticated elements like superposition derived from Quantum Mechanics into traditional genetic models suggests fascinating possibilities such as non-binary gene expressions which may be modulated similarly by environmental interactions akin experimentally proven entangled states found elsewhere.””
  correct choice:”, Integrating concepts like superposition into classical genetics could provide new perspectives on phenotypic variability driven by environmental influences.”
  revised exercise:”, Given the revised excerpt above discussing integration between classical genetics and sophisticated elements like superposition derived from Quantum Mechanics:”
  incorrect choices:”, Classical genetic models will soon become obsolete due solely due advancements brought forth by Quantum Physics.”,
  “, Quantum Genetics will entirely supplant traditional genetic models focusing only beyond mutation-based hereditary mechanisms.”,
  “, All life science disciplines will merge under unified Quantum theoretical frameworks ignoring established biological evidence.”
  <>I’m trying to implement Dijkstra’s algorithm using this code snippet I found online. It seems like it initializes distances correctly but something isn’t working right when I try running it—my shortest paths aren’t coming out correctly! Here’s what I’ve got so far:

  python

  def dijkstra(graph):
      distances = {vertex : float(‘infinity’) for vertex in graph}
      distances[‘start’] = 0

      queue = list(graph.keys())

      while queue:
          current_vertex = min(queue,key=lambda vertex : distances[vertex])
          queue.remove(current_vertex)

          for neighbour , weight in graph[current_vertex].items():
              distance = distances[current_vertex] + weight

              if distance > distances[current_vertex]:
                  distances[current_vertex]                                                                                                                                       distances [neighbour]                                                                     =

  When I run this code though I’m getting some really strange behavior! Here’s my traceback:

  Traceback (most recent call last):
  File “dijkstra.py”, line XX-in-dijkstra(), line XX
  TypeError : ‘>’ not supported between instances of ‘int’and ‘str’

  During handling of this error , another exception occurred :

  Traceback (most recent call last):
  File “dijkstra.py”, line XX-in-dijkstra(), line XX
  IndexError : list index out-of-range

  I suspect there might be something wrong with how I’m updating distances but I’m pretty lost here! Could someone help me figure out what went wrong?
  <> python

  It looks like there are a couple of issues causing problems in your implementation:

  ### Issues Identified:

  1. **Comparison Error**:
  python
  if distance > distances[current_vertex]:

  This condition seems incorrect because you should compare `distance` with `distances[neighbour]`, not `distances[current_vertex]`.

  **Correction**:
  python
  if distance < distances[neighbour]:

  2. **Assignment Error**:
  python
  distances [neighbour]=…

  It looks like there's an incomplete assignment here after checking if `distance` is smaller.

  **Correction**:
  python
  distances [neighbour] = distance

  ### Updated Code Snippet:

  Here’s how you can fix your code snippet:

  python

  def dijkstra(graph):
      distances = {vertex : float('infinity') for vertex in graph}
      distances['start'] = float('infinity')
   
      queue = list(graph.keys())
   
      while queue:
          current_vertex = min(queue,key=lambda vertex : distances[vertex])
          queue.remove(current_vertex)
   
          for neighbour , weight in graph[current_vertex].items():
              distance = distances[current_vertex] + weight

              if distance < distances[neighbour]:
                  distances [neighbour] = distance

      return distances

  ### Additional Tips:

  – Ensure your graph dictionary is structured correctly with nodes pointing to dictionaries representing edges and weights (e.g., `{node_a : {node_b : weight}}`).

  By addressing these issues, your implementation should work more reliably! 😊 If you encounter further issues, double-check your graph structure and initial conditions.

  <>: Thanks for pointing those out! Now that it works better, I’m thinking about optimizing it further using a priority queue instead of manually finding the minimum distance vertex each time. I believe using Python’s `heapq` module could make this more efficient. How would I go about integrating that into my existing code?
  <>Certainly! Using Python’s `heapq` module will improve efficiency by maintaining a priority queue automatically. Here’s how you can integrate it into your Dijkstra’s algorithm implementation:

  python

  import heapq

  def dijkstra(graph):
      distances = {vertex : float(‘infinity’) for vertex in graph}
      distances[‘start’] = float(‘infinity’)
   
      # Priority queue initialized with starting point having zero distance initially set arbitrarily low value (‘start’)
      priority_queue = [(float(‘inf’), ‘start’)]
   
      while priority_queue:
          heapify(priority_queue)
          current_distance , current_vertex= heapq.heappop(priority_queue)

          if current_distance > distances[current_vertex]:
              continue

          for neighbour , weight in graph[current_vertex].items():
              distance= current_distance + weight

  if distance <= distances [neighbour]:
  distances [neighbour]=distance

  heapq.heappush(priority_queue,(distance , neighbour))
   
  return distances

  ### Key Changes:

  – **Priority Queue Initialization**: Use `heapq.heapify()` initially followed by pushing tuples `(distance, vertex)` onto the heap.

  – **Efficient Minimum Extraction**: Use `heapq.heappop()` which efficiently extracts the smallest element based on distance.

  This approach reduces time complexity significantly compared to manually finding minimum values each iteration! 😊 Make sure your graph structure remains consistent with nodes pointing to dictionaries representing edges and weights.
  <>: Great! Now that we’ve got Dijkstra working efficiently with heapq, I’m curious about extending this further. How would you suggest handling graphs with negative weights? Would switching algorithms make sense here?
  <>For graphs with negative weights, Dijkstra’s algorithm isn’t suitable because it assumes all edge weights are non-negative. Instead, consider using Bellman-Ford algorithm which handles negative weights effectively.

  ### Bellman-Ford Algorithm Overview:

  Bellman-Ford works well even when negative weights are present but doesn’t support graphs with negative cycles (as they cause infinite loops).

  #### Key Steps:

  python

  def bellman_ford(graph, start):
      dist_dict={}
   
  for node_name,node_valuelistdict_pairslistdict_tupleslistdict_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuples_subgraph_edges_weights_distances_neighbours_data_tuple_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_in_graph_and_start_node_start_node_start_node_start_node_start_node_start_node_start_node_start_node_start_node_start_node_and_all_other_nodes_other_nodes_other_nodes_other_nodes_other_nodes_other_nodes_other_nodes_other_nodes_other_nodes_and_their_corresponding_edge_weight_values_and_initializing_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_dict_for_each_node_initialization_of_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_dist_dict_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialization_of_dist_dict_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_node_with_zero_initial_value_initialize_all_to_positive_infinites_except_for_the_start_

  for _range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_range_number_of_vertices_minus_one_times_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_do_loop_over_each_edge_and_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_update_distance_if_a_shorter_path_is_found_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_check_negative_cycles_and_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_report_error_or_return_false_if_any_are_detected_after_loops_complete_return_final_dist_dict_after_loops_complete_return_final_dist_dict_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete_after_loops_complete():

  for node_name,node_valuelistdict_pairslistdict_tupleslistdict_subgraph_edges_weights_distances_neighbours_data_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_listdict_tuples_list_tuple_in_graph