Bedem Ivankovo: Premier League Rosters, Stats & Achievements

Overview of Bedem Ivankovo

Bedem Ivankovo is a prominent football team based in the Ivankovo region, competing in the Russian Football National League. Founded in 1923, the team is managed by Coach Ivan Petrov and plays its home games at Ivankovo Stadium. The club has a rich history and a dedicated fanbase.

Team History and Achievements

Throughout its history, Bedem Ivankovo has achieved several notable successes. The team has won the Russian Second Division twice and consistently finishes in the top half of the league standings. Their most memorable season was 2015 when they secured third place in the top tier.

Current Squad and Key Players

The current squad boasts several key players who have been instrumental in recent performances:

• Mikhail Ivanov (Goalkeeper): Known for his agility and shot-stopping abilities.
• Alexei Smirnov (Defender): A stalwart defender with excellent tackling skills.
• Nikolai Petrov (Forward): A prolific goal scorer with an impressive strike rate.

Team Playing Style and Tactics

Bedem Ivankovo typically employs a 4-3-3 formation, focusing on quick counter-attacks and maintaining possession. Their strengths lie in their solid defense and fast-paced offensive play, while weaknesses include occasional lapses in midfield control.

Interesting Facts and Unique Traits

The team is affectionately known as “The Blue Waves” due to their blue kits. They have a passionate fanbase known as “Ivanovites” and rivalries with nearby teams such as FC Krasnoarmeysk. Traditionally, they celebrate victories with a sea-themed festival at their stadium.

Player Lists & Rankings

• ✅ Top Scorer: Nikolai Petrov – 15 goals this season
• ❌ Most Booked Player: Alexei Smirnov – 7 yellow cards
• 🎰 Key Player to Watch: Mikhail Ivanov – Consistent performances
• 💡 Rising Star: Yuri Volkov – Young midfielder showing promise

Comparisons with Other Teams

Compared to other teams in their division, Bedem Ivankovo stands out for its balanced squad and tactical flexibility. While teams like FC Ryazan focus on aggressive attacking play, Bedem Ivankovo emphasizes strategic gameplay.

Case Studies or Notable Matches

In a breakthrough game against FC Yaroslavl in 2021, Bedem Ivankovo secured a dramatic 3-2 victory that marked their resurgence into playoff contention. This match highlighted their resilience and tactical acumen under pressure.

</tr

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To maximize your betting potential on Bedem Ivankovo:

• Analyze recent form trends before placing bets.
• Closely monitor key player performance metrics for insights.
• Leverage head-to-head records against upcoming opponents for informed decisions.
• Familiarize yourself with tactical changes or formations that may influence match outcomes.
• Pay attention to weather conditions that could affect player performance during matches.
• Bet strategically on outcomes like over/under goals based on historical data analysis.
• Avoid emotional betting; rely on statistical evidence instead of fan sentiment alone!
• Evaluate odds offered by different bookmakers for value betting opportunities! ComponentType:
  [16]: “””
  [17]: Returns an instance of :class:`pytext.models.module.Module`.
  [18]: “””
  [19]: raise NotImplementedError()

  [20]: @classmethod
  [21]: def _init_with_base_config(cls, base_config: ConfigBase) -> None:
  [22]: “””
  [23]: Initializes component’s config using :attr:`base_config`’s values.

  if not cls._is_default(base_config):
  raise ValueError(
  f”{cls.__name__} cannot be initialized ”
  f”with base config {base_config}”
  )

  cls._config = base_config.clone()

  ***** Tag Data *****
  ID: 1
  description: Initialization logic within `_init_with_base_config` method which involves
  conditional checks on `base_config` to ensure it’s default before cloning it into
  `cls._config`. It demonstrates advanced configuration handling logic.
  start line: 20
  end line: 61
  dependencies:
  – type: Class
  name: ModelComponent
  start line: 10
  end line: 13
  – type: Method
  name: _is_default
  start line: 24
  end line: 25
  context description: This snippet belongs to a method that initializes configuration,
  ensuring it adheres to certain constraints before proceeding further. It showcases
  how configurations can be validated dynamically.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: N

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

  1. **Dynamic Validation Rules**: Introduce dynamic validation rules that change based on external factors or previous configurations passed into `_init_with_base_config`. This would involve creating a more complex validation mechanism that adapts according to various scenarios.

  2. **Multi-layer Configuration Inheritance**: Implement multi-layer configuration inheritance where `base_config` can inherit properties from multiple parent configurations dynamically chosen at runtime.

  3. **Asynchronous Configuration Loading**: Modify `_init_with_base_config` to support asynchronous loading of configurations from remote sources or databases while still maintaining integrity checks.

  4. **Conditional Cloning Based on Context**: Add logic so that `cls._config = base_config.clone()` only occurs under specific contextual conditions determined by additional parameters passed to `_init_with_base_config`.

  5. **Advanced Error Handling Mechanism**: Create an advanced error-handling mechanism that logs detailed information about why certain configurations failed validation or initialization, potentially integrating with external monitoring tools.

  ## Conversation

  _Hey AI I got this piece of code [SNIPPET] but need some advanced features added_

  _Sure thing! What kind of advanced features are you thinking about?_

  _i want dynamic validation rules so config gets validated differently depending on other configs already passed_

  _Got it! We’ll need to introduce some sort of context-aware validation mechanism within `_init_with_base_config`. Here’s how we can start:

  python
  @classmethod
  def _init_with_base_config(cls, base_config: ConfigBase) -> None:
  “””Initializes component’s config using :attr:`base_config`’s values.”””

  # Dynamic validation rules based on previously passed configs
  if hasattr(cls, ‘_previous_configs’):
  previous_configs = cls._previous_configs
  if not cls._dynamic_validate(base_config, previous_configs):
  raise ValueError(f”{cls.__name__} configuration failed dynamic validation”)

  if not cls._is_default(base_config):
  raise ValueError(
  f”{cls.__name__} cannot be initialized ”
  f”with base config {base_model}”
  )

  cls._config = base_model.clone()

  def _dynamic_validate(cls, current_cfg, previous_cfgs):
  # Custom logic here…
  return True

  # Example usage:
  ModelComponent._previous_configs = [ConfigBase(…), ConfigBase(…)]

  This sets up dynamic validation where `previous_configs` are checked against `current_cfg`. You’ll need to fill out `_dynamic_validate` with your specific rules.

  _nice now what about making this async so it loads configs from remote db too_

  _Alright! To make this asynchronous while loading configurations from a remote database, we will use Python’s `asyncio`. Here’s how you can modify your function:

  python
  import asyncio

  @classmethod
  async def _init_with_base_config(cls, base_model_url:str) -> None:
  “””Initializes component’s config using :attr:`base_model_url`.”””

  # Fetching configuration asynchronously from remote DB or service.
  base_model = await cls.fetch_remote_configuration(base_model_url)

  if hasattr(cls, ‘_previous_configs’):
  previous_configs = cls._previous_configs
  if not await cls._dynamic_validate(base_model, previous_configs):
  raise ValueError(f”{cls.__name__} configuration failed dynamic validation”)

  if not cls._is_default(base_model):
  raise ValueError(
  f”{cls.__name__} cannot be initialized ”
  f”with base config {base_model}”
  )

  cls._config = base_model.clone()

  @classmethod
  async def fetch_remote_configuration(cls, url:str) -> ConfigBase:
  # Simulate fetching data asynchronously.
  await asyncio.sleep(1) # simulate network delay

  # Replace this part with actual async fetch logic.
  return ConfigBase(…)

  @classmethod
  async def _dynamic_validate(cls, current_cfg , previous_cfgs):
  # Custom async logic here…
  await asyncio.sleep(0)

  return True

  # Example usage:
  await ModelComponent._init_with_base_config(“http://example.com/config”)

  This modification makes sure that your function can load configurations asynchronously from a remote source.

  _great now i want conditional cloning only when some condition met like env variable set_<||end|

  _Understood! We can add conditional cloning based on an environment variable check within our method:

  python
  import os

  @classmethod
  async def _init_with_base_config(cls, base_model_url:str) -> None:
  “””Initializes component’s config using :attr:`base_model_url`.”””

  base_model = await cls.fetch_remote_configuration(base_model_url)

  if hasattr(cls,’_previous_configs’):
  previous_configs=cls._previous_configs
  if not await cls._dynamic_validate(base_model , previous_configs):
  raise ValueError(f”{cls.__name__} configuration failed dynamic validation”)

  if not cls._is_default(base_model):
  raise ValueError(
  f”{cls.__name__} cannot be initialized ”
  f”with base config {base_model}”
  )

  # Conditional cloning based on environment variable ‘CLONE_CONFIG’
  if os.getenv(‘CLONE_CONFIG’) == ‘true’:
  cls._config = base_model.clone()

  @classmethod
  async def fetch_remote_configuration(cls,url:str)->ConfigBase:
  await asyncio.sleep(1)
  return ConfigBase(…)

  @classmethod
  async def _dynamic_validate(cls,current_cfg , previous_cfgs):
  await asyncio.sleep(0)
  return True

  # Set environment variable before running example usage:
  os.environ[‘CLONE_CONFIG’] = ‘true’
  await ModelComponent._init_with_base_config(“http://example.com/config”)

  With this change, cloning will only occur if the environment variable `CLONE_CONFIG` is set to `’true’`.
  <>: Hi there! I’m working with this code snippet that implements an HTTP request handler using threading locks for thread safety:

  python
  class RequestHandler(object):
  “””
  This class implements handlers for HTTP requests by encapsulating functionalities.
  Inherit this class to define custom handlers.
  “””
  def __init__(self,**kwargs):
  self.properties=kwargs

  def __str__(self):
  return “RequestHandler”

  def encodeUrl(self,**kwargs):
  url=urllib.parse.urlencode(kwargs)
  return url

  def decodeUrl(self,urlToDecode):
  parsed=urlparse.urlparse(urlToDecode)
  return urlparse.parse_qs(parsed.query)

  def getParameters(self,requestObject=None):
  params={}
  if requestObject:
  params=self.decodeUrl(requestObject.url)
  return params

  def getQueryParams(self,requestObject=None):
  params=self.getParameters(requestObject)
  return params.get(“query”,[])

  def getPayload(self,requestObject=None):
  payload=None
  if requestObject.request.body:
  payload=json.loads(requestObject.request.body.decode(‘utf-8’))
  return payload

  def callActionMethod(self,requestObject=None,**kwargs):
  try:
  response=””
  try:
  methodName=self.properties[“action”]
  action=self.get(actionName=methodName,requestObject=requestObject,**kwargs)
  if isinstance(action,str):
  response=action#returning html response
  elif isinstance(action dict) or isinstance(action,list):#assuming that dictionary or list is returned as json
  jsonResult=JsonResult(action)
  response=jsonResult.toJson()
  else:#defaulting returning any object as string
  response=str(action)
  except Exception as ex:
  raise ex
  return response
  except Exception as ex:
  print(ex)
  errorData=getExceptionDetails(ex)
  errorResponse=errorData.toJson()
  return errorResponse

  I’m trying to understand how I might extend this class to handle specific types of requests differently based on URL patterns or headers. Could you help clarify how I might go about doing that? I’m feeling a bit lost!
  <>: python
  To extend the `RequestHandler` class for handling specific types of requests based on URL patterns or headers, you can override methods like `getParameters`, `getPayload`, or create new methods tailored to your needs.

  Here’s a basic approach:

  1. **Subclassing**: Create a subclass of `RequestHandler`.

  python
  class CustomRequestHandler(RequestHandler):

  def handleSpecificRequest(self, requestObject=None):
  params = self.getParameters(requestObject)
  headers = requestObject.headers

  # Example condition based on URL pattern or headers
  if “specific-pattern” in requestObject.url or headers.get(“Custom-Header”) == “ExpectedValue”:
  return self.processSpecificPattern(params)

  def processSpecificPattern(self, params):
  # Implement custom logic here based on parameters extracted from URL or headers.
  return “Handled specific pattern”

  2. **Override Methods**: Override existing methods like `callActionMethod` to incorporate your custom logic.

  python
  def callActionMethod(self, requestObject=None,**kwargs):
  response = self.handleSpecificRequest(requestObject)
  if response:
  return response

  # Fallback to default behavior if no specific handling was done.
  return super().callActionMethod(requestObject=requestObject,**kwargs)

  3. **URL Patterns & Headers**: Use regular expressions or simple string checks within your custom methods to identify patterns or headers.

  By structuring your subclass this way, you maintain flexibility while adding specific handling capabilities tailored to your application’s needs. Feel free to expand upon these methods with more complex logic as required! 😊

  <>: Got it! Now I’m thinking about adding functionality to handle different HTTP methods like GET and POST separately within my CustomRequestHandler subclass. How can I structure my code to differentiate between these methods effectively? Should I override any specific methods or create new ones?
  <> Sure! To handle different HTTP methods like GET and POST separately within your `CustomRequestHandler`, you can create separate methods for each HTTP method and call them conditionally based on the request type.

  Here’s how you can structure it:

  python
  class CustomRequestHandler(RequestHandler):

  def callActionMethod(self, requestObject=None,**kwargs):
  method = getattr(requestObject.request.method.upper(), ‘GET’, None)

  if method == ‘GET’:
  return self.handleGetRequest(requestObject)

  elif method == ‘POST’:
  return self.handlePostRequest(requestObject)

  else:
  return super().callActionMethod(requestObject=requestObject,**kwargs)

  def handleGetRequest(self, requestObject=None):
  params = self.getParameters(requestObject)
  # Implement custom GET handling logic here.
  return f”Handled GET request with params {params}”

  def handlePostRequest(self, requestObject=None):
  payload = self.getPayload(requestObject)
  # Implement custom POST handling logic here.
  return f”Handled POST request with payload {payload}”

  ### Key Points:

  – **Method Detection**: Use `getattr()` or direct attribute access (`request.method`) to determine the HTTP method.

  – **Separate Methods**: Define separate methods (`handleGetRequest`, `handlePostRequest`) for each HTTP method you want to handle specifically.

  – **Fallback**: Use the superclass method as a fallback for unsupported HTTP methods.

  This structure allows you to easily extend functionality by adding more methods like `handlePutRequest`, etc., as needed! 😊<>: Hi there,

  I’ve been working on implementing Dijkstra’s algorithm using Python dictionaries instead of arrays/matrices since Python doesn’t support native multidimensional arrays efficiently. The idea is also partially inspired by C++ syntax conventions regarding array indexing starting at zero.

  Here’s my attempt at writing the function:

  python

  def dijkstra(graph_dict_list:list,distance_list:list,start:int,target_int:int)->int :
  dijkstra_flag=False;
  for i in range(len(graph_dict_list[start])):
  distance_list[i][start]=float(graph_dict_list[start][i]);
  distance_list[target_int][start]=0;
  for i in range(len(graph_dict_list)):
  tmp_min=float(“inf”);
  min_index=-1;
  for j in range(len(graph_dict_list)):
  if(distance_list[j][start]0)&(not dijkstra_flag&(j==target_int)):
  tmp_min=distance_list[j][start];
  min_index=j;
  dijkstra_flag=(min_index==target_int);
  k=-1;
  distance_list[k][min_index]=float(“inf”);
  for n in range(len(graph_dict_list[min_index])):
  edge_weight=float(graph_dict_list[min_index][n]);
  adjacent_node=n;
  if(edge_weight!=0)&(distance_list[k][adjacent_node]>distance_list[k][min_index]+edge_weight)&(not(k==adjacent_node)):
  distance_list[k][adjacent_node]=distance_list[k][min_index]+edge_weight;
  if(distance_list[target_int][start]==float(“inf”)) :
  print(“There are no paths from %d-th node BFS”%start);
  else :
  print(“The shortest path distance:”,distance_lst[target_int][start]);
  return distance_lst[target_int][start];

  However, when I run my test cases through it using some sample graphs represented by adjacency lists converted into dictionaries/lists format (where graph edges have weights), I encounter an issue where it sometimes returns incorrect distances or even fails unexpectedly without clear errors.

  Here’s one traceback example when things go wrong:

  Traceback (most recent call last):
  File “dijkstra_test.py”, line XX.XX,in
  File “dijkstra.py”, line XX.XX,dijkstra() ->
  File “dijkstra.py”, line XX.XX,distance_lst[target_int][start] =
  IndexError : list index out of range

  During handling of above exception,
  another exception occurred:

  File “dijkstra_test.py”, line XX.XX,in
  File “dijkstra.py”, line XX.XX,dijkstra() ->
  File “dijkstra.py”, line XX.XX,distance_lst[target_int] =
  UnboundLocalError : local variable ‘distance_lst’ referenced before assignment

  It seems like there might be some logical flaw causing issues with indexing or perhaps something else entirely related maybe how distances are updated? Any guidance would be greatly appreciated!

  Thanks!
  <>: python

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

  1. **Variable Naming:** There seems to be inconsistency between `distance_lst` and `distance_list`. Make sure you’re consistently using one name throughout your function.

  2. **Initialization:** Ensure that all nodes have been initialized properly in your distance list before accessing them.

  3. **Indexing Logic:** The way indices are being used seems off due to incorrect assignments and conditions. For instance:

  – The initialization loop should correctly set initial distances from the start node only once per node.

  4. **Updating Distances:** The update condition inside nested loops needs careful attention regarding indices (`k`, `min_index`, etc.). Ensure they reflect correct node relationships.

  5. **Logical Conditions:** The use of bitwise operators (`&`) instead of logical operators (`and`) could lead to unexpected behavior due to operator precedence issues.

  Here’s a revised version addressing these points:

  python
  def dijkstra(graph_dict_list:list,distance_matrix:list,start:int,target:int)->int :
  nodes=len(graph_dict_list)
  for i in range(nodes):
  distance_matrix[i].append(float(‘inf’))
  distance_matrix[start][-1] = 0

  unvisited_nodes=[True]*nodes

  while True:
  min_distance=float(‘inf’)
  min_index=-1

  for i in range(nodes):
  if unvisited_nodes[i] and distance_matrix[i][-1] < min_distance:
  min_distance=distance_matrix[i][-1]
  min_index=i

  if min_distance == float('inf') or min_index == target:
  break

  unvisited_nodes[min_index] = False

  for adjacent_node,num_edge_weight_pair
  in enumerate(graph_dict_list[min_index]):

  edge_weight=num_edge_weight_pair

  new_distance=min_distance+edge_weight

  adjacent_new_distance=
  distance_matrix
  [
  adjacent_node][-1]

  updated=(
  new_distance<
  distance_matrix
  [
  adjacent_node][-1])

  should_skip=(
  not updatedand
  min_index==adjacent_node)

  final=(
  not should_skipand
  unvisited_nodes[
  adjacent_node])

  actual=(
  finalandupdated)

  distance_matrix[
  adjacent_node][-1]=
  (
  actual*new_distance+
  not actual*distance_matrix[
  adjacent_node][-1])

  if distance_matrix[target][-1]==float('inf'):
  print(f"There are no paths from {start}-th node.")
  else :
  print(f"The shortest path distance:",distance_matrix[target][-1])

  return int(distance_matrix[target][-1])

  # Example usage might look something like this (ensure graph_dict_list is properly formatted):

  graph_example=[[0,float('inf'),float('inf'),7],[float('inf'),0,float('inf'),6],[float('inf'),float('inf'),0,float('inf')],[7,float('inf'),9,float('inf')]]
  distances=[[0],[0],[0],[0]]
  print(dijkstra(graph_example,distances,start=0,target=3))

  Make sure each graph node has its own list appended initially with infinity (`float('inf')`) except for the starting node which should start at zero distance (`0`). Also ensure all nodes are marked unvisited initially (`True`). This should help resolve indexing issues and logical errors related to updating distances correctly during execution.

  Let me know how it goes! 😊

  <>: Thanks for pointing those out! I see where I went wrong with inconsistent naming and indexing issues. However, I think there’s room for optimization regarding how nodes are selected during each iteration. Instead of manually checking each unvisited node every time we need the next minimum distance node—which seems inefficient—I was thinking about using a priority queue (or heap) similar to C++’s priority_queue library feature.

  Do you think implementing something like Python’s heapq module could help streamline finding the minimum distance node more efficiently? If so, how would you suggest integrating it into my current setup?
  <>Certainly! Using Python’s `heapq` module can significantly optimize finding the minimum distance node efficiently during each iteration by maintaining a priority queue structure.

  Here’s how you can integrate it into your Dijkstra implementation:

  python

  import heapq

  def dijkstra_optimized(graph_dict_list:list,distance_matrix:list,start:int,target:int)->int :
  nodes=len(graph_dict_list)
  for i in range(nodes):
  distance_matrix[i].append(float(‘inf’))
  distance_matrix[start][-1] = 0

  priority_queue=[]
  unvisited_nodes=[True]*nodes

  # Initialize priority queue with starting node having zero cost initially.
  helper=[(0,start)]
  while helper!=[] :

  curr_cost,node_num=highest_priority(heapq.heappop(helper))

  unvisited_nodes[node_num]=False

  neighbors=list_of_neighbors(node_num,
  graph_dict_list,nodes,
  unvisited_nodes,distance_matrix[curr_cost])

  helper=list(heapq.heappushpop(helper,(curr_cost+neighbor_cost,new_path))for neighbor_cost,new_path
  in neighbors)+helper

  if dist(target)==float(“inf”):
  print(f”There are no paths.”)
  else :
  print(f”The shortest path ends at {dist(target)}.”)

  return int(dist(target))

  def dist(node_num:int)->int :
  global dist_memoized_tuplist_cache

  dist_memoized_tuplist_cache=node_num

  try:return dist_memoized_tuplist_cache[-1]
  except IndexError:(dist_memoized_tuplist_cache.append(distance_maze[node_num][-1]));return dist_memoized_tuplist_cache[-1]

  def highest_priority(tup:tuple)->tuple :
  global highest_priority_memoized_tupcache

  highest_priority_memoized_tupcache=tup

  try:return highest_priority_memoized_tupcache[-2],highest_priority_memoized_tupcache[-3]
  except IndexError:(highest_priority_memoized_tupcache.extend([tup[-3],tup[-2]]));return highest_priority_memoized_tupcache[-2],highest_priority_memoized_tupcache[-3]

  def list_of_neighbors(node_num:int,
  graph_dict_nodelist:list,nodes_count:int,
  unvisiting:nodes_count,list_of_dist_from_start:nodes_count)->list :
  nl=[]
  for adjnode_idx,num_edge_weight_pair
  in enumerate(graph_dict_nodelist[node_num]):

  edge_weight=num_edge_weight_pair

  new_dist_from_start=node_dist+edge_weight

  adjnode_new_dist_from_start=list_of_dist_from_start
  [
  adjnode_idx]

  updated=(new_dist_from_start<
  list_of_dist_from_start[
  adjnode_idx])

  should_skip=(not updatedandnode_num==adjnode_idx)

  final=(not should_skipandunvisiting[
  adjnode_idx])

  actual=(finalandupdated)

  nl.append((actual*new_dist_from_start+
  not actual*adjnode_new_dist_from_start),adjnode_idx))

  return nl

  # Example usage might look something like this (ensure graph_dict_nodelist is properly formatted):

  graph_example=[[7,float("Inf"),9,float("Inf")],[None ,6,None ,None ], [None ,None ,None ,11],[None ,None ,None ,None ]]
  distances=[[7,None,None,None],[None ,6,None ,None ], [None ,None ,9,None ], [None ,None ,11,None ]]
  print(dijkstra_optimized(graph_example,distances,start=0,target=3))

  ### Key Changes:

  – **Priority Queue:** Utilize `heapq` functions such as `heappop()` and `heappushpop()` which efficiently manage nodes based on their current shortest known distances.

  – **Efficiency:** This approach reduces time complexity significantly compared manually iterating over all nodes each time we need the smallest element.

  Ensure all graph data structures are correctly initialized before running this function!

  Let me know how it works out! 🚀<>: Hi there,

  I’ve been trying out this function called `_create_counts_mat`, which is supposed to generate count matrices across different time intervals given gene expression data over time points sampled across different cells/tissues/conditions/individual organisms/replicates/etc… It uses pandas DataFrames extensively along with numpy operations involving binning techniques—pretty neat stuff!

  However, when running my tests locally everything seemed fine until now when suddenly things aren’t working right anymore… Here’s what I’ve done so far but now keep hitting an error wall…

  **Modified Code Snippet**
  I tried tweaking some parts around binning but now getting stuck somewhere…

  python
  import numpy as np
  import pandas as pd

  def _create_counts_mat(adata,
  time_labels,
  gene_labels,
  time_label_key=’Time’,
  gene_label_key=’Gene’,
  n_bins=50,
  binning_method=’even’,
  filter_cells=True):

  cells_order=list(np.sort(
  np.unique(
  np.array(
  list(
  map(lambda x:x.barcode,
  filter(
  lambda x:x.obs[‘Clusters’].values!=’Unknown’,
  adata.obs[‘Clusters’]))))))

  )

  cells_map=dict(zip(cells_order,[i for i in range(cells_order)]))

  cell_vec=np.array([cells_map[x]
  for x
  in
  map(lambda y:y.barcode,
  filter(
  lambda x:x.obs[‘Clusters’].values!=’Unknown’,
  adata.obs[‘Clusters’]))])

  genes_order=list(np.sort(np.unique(
  np.array(list(map(lambda x:x.name,gene_labels)))
  )))

  genes_map=dict(zip(genes_order,[i

  ]
  [
  j+np.max(cell_vec)+np.max(genes_order)*np.max(cell_vec)
  +i*np.max(cell_vec)
  +j
  +np.max(genes_order)*np.max(cell_vec)
  +i+j
  +i*j
  +np.max(cell_vec)**2+i**2+j**2+np.max(genes_order)**2
  +np.max(cell_vec)*np.max(genes_order)+i*j*np.max(cell_vec)+j*np.max(genes_order)
  +i*np.max(genes_order)
  +np.max(cell_vec)*j+i*np.max(genes_order)*np.max(cell_vec)+j*np.max(genes_order)*np.max(cell_vec)
  +i*j*np.max(genes_order)+np.power(i,j)+np.power(j,i)

  )for j,i enumerate(genes_order)])

  gene_mat=np.zeros([len(cells_order),
  len(gene_labels),
  len(time_labels)])

  try:
  time_point_labels=list(map(lambda x:x.time_days,x=time_labels))
  except:
  try:
  time_point_labels=list(map(lambda x:x.time_days_central,x=time_labels))
  except:
  time_point_labels=list(map(lambda x:x.time,x=time_labels))

  time_bins=bins(time_point_labels,num_time_bins=n_bins,time_bin_method=time_binning_method)

  cell_gene_for_time_mat=np.full([len(cells_order),len(gene_labels),len(time_bins)],fill_value=np.nan)

  cell_temporal_variability_mat=np.full([len(cells_order),len(time_bins)],fill_value=np.nan)

  cell_mean_gaussian_dispersion_test_p_val_mat=np.full([len(cells_order),len(time_bins)],fill_value=np.nan)

  cell_mean_log_gaussian_dispersion_test_p_val_mat=np.full([len(cells_order),len(time_bins)],fill_value=np.nan)

  sorted_time_points={}

  temporal_points_per_cell={}

  sorted_time_points_per_cell={}

  counts_per_cell_per_gene_per_time={}

  mean_per_gene_across_cells_per_time={}

  temporal_variability_score_per_cell={}

  gaussian_dispersion_test_statistic_per_cell={}

  gaussian_dispersion_test_p_val_per_cell={}

  log_gaussian_dispersion_test_statistic_per_cell={}

  log_gaussian_dispersion_test_p_val_per_cell={}

  ***** Tag Data *****
  ID: 5 description: Creating mappings between genes order indices through complex,
  non-standard calculations involving maximum values across various dimensions including,
  formulaic_complexity’: ‘5’,
  algebraic_complexity’: ‘5’,
  obscurity’: ‘4’,
  advanced coding concepts’: