Shelbourne FC: Premier Division Squad, Stats & Achievements

Overview / Introduction about Shelbourne Football Team

Shelbourne Football Club, often referred to as the “Boys in Blue,” is a historic Irish football team based in Dublin. Founded in 1895, the club competes in the League of Ireland Premier Division. Known for its passionate fanbase and rich history, Shelbourne has been a staple in Irish football.

Team History and Achievements

Shelbourne boasts an illustrious history with numerous titles to their name. The club has won the League of Ireland title 11 times and secured the FAI Cup on six occasions. Notable seasons include their treble-winning campaign in 1963-64, where they clinched the league, cup, and European Cup Winners’ Cup.

Current Squad and Key Players

The current squad features several standout players who are pivotal to Shelbourne’s performance. Key players include:

• Captain John Doe: Striker known for his goal-scoring prowess.
• Midfield Maestro Jane Smith: Creative midfielder with exceptional vision.
• Defensive Anchor Mike Brown: Reliable center-back with strong aerial ability.

Team Playing Style and Tactics

Shelbourne typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their attacking play and set-piece proficiency, while weaknesses may include vulnerability at the back against counter-attacks.

Interesting Facts and Unique Traits

Nicknamed “The Blues,” Shelbourne has a dedicated fanbase known for their unwavering support. The club has a fierce rivalry with Shamrock Rovers, often referred to as the “Dublin Derby.” Traditions include pre-match songs that energize both players and fans.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: John Doe (🎰) – 15 goals this season.
• Assists Leader: Jane Smith (💡) – 10 assists this season.
• Best Defender: Mike Brown (✅) – Lowest goals conceded when playing.

Comparisons with Other Teams in the League or Division

In comparison to other top teams like Bohemians and Dundalk, Shelbourne’s aggressive style sets them apart. While Bohemians focus on possession-based play, Shelbourne thrives on quick counter-attacks and set-pieces.

Case Studies or Notable Matches

A breakthrough game was their victory against Dundalk in 2019, where they overturned a two-goal deficit to win 3-2. This match showcased their resilience and tactical flexibility under pressure.

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

Tips & Recommendations for Analyzing the Team or Betting Insights

To analyze Shelbourne effectively:

• Analyze recent form: Look at head-to-head records against upcoming opponents.
• Evaluate key player performance: Focus on top scorers and assist leaders.
• Bet on set-pieces: Given their strength in this area, consider betting on goals from corners or free-kicks.

Frequently Asked Questions (FAQ)

What is Shelbourne’s best chance for success this season?

Their attacking prowess makes them strong contenders for breaking into the top three positions if they maintain consistency against mid-table teams.

How does Shelbourne fare against their rivals?

In recent years, they’ve shown improved performances against rivals like Shamrock Rovers but still face challenges due to defensive lapses.

Are there any key injuries affecting Shelbourne?

No significant injuries have been reported recently; however, keeping an eye on fitness updates is crucial before placing bets.

Quotes or Expert Opinions about the Team

“Shelbourne’s ability to adapt tactically gives them an edge over many opponents,” says former manager Liam O’Brien. “Their resilience makes them unpredictable.”

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

• ✅ Strong attacking lineup capable of scoring multiple goals per match.
• ✅ High morale among players due to recent victories boosting confidence levels further ahead of critical fixtures.</li
  <>: Hi there! I’m working with a function called `create_gauss`, which is supposed to generate Gaussian profiles using some parameters from `parinfo`. Here’s the code snippet:

  python
  def create_gauss(parinfo=None):
  “””
  Makes a gaussian profile from parinfo dict
  :param parinfo: dictionary containing Gaussian parameters
  returns: gaussian function
  “””
  if parinfo is None:
  raise ValueError(“Gaussian parameter dictionary must be inputted with keyword parinfo”)
  A = parinfo[“amplitude”].value
  x0 = parinfo[“center”].value
  fwhm = parinfo[“fwhm”].value
  std = fwhm/np.sqrt(2*np.log(2))
  return lambda x: A*np.exp(-(x-x0)**2/(std**2))

  I’m curious about how I might extend this function to handle multiple Gaussian profiles at once. Do you have any suggestions on how I could modify it? Thanks!
  <>: python
  You can extend your function to handle multiple Gaussian profiles by iterating over a list of `parinfo` dictionaries. Here’s one way to do it:

  python
  import numpy as np

  def create_multi_gauss(parinfos=None):
  “””
  Creates multiple Gaussian profiles from a list of parinfo dictionaries.

  :param parinfos: list of dictionaries containing Gaussian parameters
  :returns: function that sums all Gaussian profiles
  “””
  if parinfos is None:
  raise ValueError(“List of Gaussian parameter dictionaries must be inputted with keyword parinfos”)

  def gauss_sum(x):
  result = np.zeros_like(x)
  for parinfo in parinfos:
  A = parinfo[“amplitude”].value
  x0 = parinfo[“center”].value
  fwhm = parinfo[“fwhm”].value
  std = fwhm / np.sqrt(2 * np.log(2))
  result += A * np.exp(-(x – x0) ** 2 / (std ** 2))
  return result

  return gauss_sum

  # Example usage:
  # gauss_func = create_multi_gauss([parinfo1, parinfo2])
  # y_values = gauss_func(x_values)

  This version takes a list of `parinfos` and returns a function that computes the sum of all specified Gaussian profiles at given `x` values. 😊

  <>: Great suggestion! Now I’m thinking about optimizing this further by allowing vectorized operations instead of looping through each profile individually. This should help improve performance when dealing with large datasets. Any ideas on how I could implement that?
  <>: Sure! You can use NumPy’s vectorized operations to optimize the computation. Here’s how you can modify your function:

  python
  import numpy as np

  def create_vectorized_gauss(parinfos=None):
  “””
  Creates multiple Gaussian profiles using vectorized operations.

  :param parinfos: list of dictionaries containing Gaussian parameters
  :returns: function that computes all Gaussian profiles simultaneously
  “””
  if parinfos is None:
  raise ValueError(“List of Gaussian parameter dictionaries must be inputted with keyword parinfos”)

  def gauss_sum(x):
  amplitudes = np.array([par[‘amplitude’].value for par in parinfos])
  centers = np.array([par[‘center’].value for par in parinfos])
  fwhms = np.array([par[‘fwhm’].value for par in parinfos])

  stds = fwhms / np.sqrt(2 * np.log(2))

  # Vectorized computation for all Gaussians at once
  exponentials = -(x[:, None] – centers) ** 2 / stds ** 2

  return amplitudes * np.exp(exponentials).sum(axis=1)

  return gauss_sum

  # Example usage:
  # gauss_func = create_vectorized_gauss([parinfo1, parinfo2])
  # y_values = gauss_func(np.array(x_values))

  This version uses NumPy arrays to perform operations across all profiles simultaneously, which should enhance performance significantly when handling large datasets.
  <>: Awesome! Now I’m considering leveraging parallel processing to speed things up even more when dealing with really large datasets. How could I integrate something like multiprocessing or another parallel computing approach into this setup?
  <>: python
  You can use Python’s `multiprocessing` module to parallelize computations across multiple processes. Here’s how you might integrate it:

  python
  import numpy as np
  from multiprocessing import Pool

  def compute_gaussian(params):
  A, x0, std, x_chunk = params
  return A * np.exp(-(x_chunk – x0) ** 2 / std ** 2)

  def create_parallel_gauss(parinfos=None):
  “””
  Creates multiple Gaussian profiles using parallel processing.

  :param parinfos: list of dictionaries containing Gaussian parameters
  :returns: function that computes all Gaussian profiles using parallel processing
  “””

  if parinfos is None:
  raise ValueError(“List of Gaussian parameter dictionaries must be inputted with keyword parinfos”)

  def gauss_sum(x):
  amplitudes = [par[‘amplitude’].value for par in parinfos]
  centers = [par[‘center’].value for par in parinfos]
  fwhms = [par[‘fwhm’].value for par in parinfos]

  stds = [fwhm / np.sqrt(2 * np.log(2)) for fwhm in fwhms]

  # Prepare data for parallel processing
  params_list = [(A, x0, std, x) for A, x0, std in zip(amplitudes, centers, stds)]

  # Use multiprocessing Pool to compute Gaussians in parallel
  with Pool() as pool:
  results = pool.map(compute_gaussian_chunked(params_list), np.array_split(x))

  # Combine results from different processes
  return sum(results)

  def compute_gaussian_chunked(params_list):
  def wrapper(x_chunk):
  partial_results = [compute_gaussian((A,x0,std,x_chunk)) for A,x0,std,_ in params_list]
  return sum(partial_results)
  return wrapper

  # Example usage:
  # gauss_func = create_parallel_gauss([parinfo1, parinfo2])
  # y_values = gauss_func(np.array(x_values))

  This setup uses `multiprocessing.Pool` to distribute chunks of data across available CPU cores. Each process computes part of the total result independently before combining them. This approach should help speed up computations significantly on large datasets! 🚀

  <>: This looks promising! To make it even more efficient across different systems with varying core counts and memory availability, how about dynamically adjusting the number of processes based on available resources? Any tips on implementing that?
  <>: python
  Certainly! You can dynamically adjust the number of processes by using `os.cpu_count()` to determine available CPU cores and then decide how many processes to use based on system resources.

  Here’s an updated version:

  python
  import numpy as np
  from multiprocessing import Pool, cpu_count

  def compute_gaussian(params):
  A, x0, std, x_chunk = params
  return A * np.exp(-(x_chunk – x0) ** 2 / std ** 2)

  def create_dynamic_parallel_gauss(parinfos=None):
  “””
  Creates multiple Gaussian profiles using dynamic parallel processing.

  :param parinfos: list of dictionaries containing Gaussian parameters

  :returns: function that computes all Gaussian profiles using dynamic parallel processing

  “””

  if not isinstance(parinfos,list):
  raise ValueError(“List of Gaussian parameter dictionaries must be inputted with keyword ‘parInfos'”)

  def gauss_sum(x):
  amplitudes = [par[‘amplitude’].value for parsinfs]
  centers =[parsinf[‘center’] . valuefor parsinfs]
  fwhms =[parsinf[ ‘fwhm’ ] . valuefor parsinfs]

  stds =[fwm/np.sqrt( 28*ln( ) )for fwmmn ]

  # Prepare data for parallel processing
  params_list=[(A,x0,std,x)forA,x0,stdinzip(amplitudes ,centers ,stdds)]

  # Determine optimal number of processes
  num_processes=min(cpu_count(),len(params_list)//10+1)

  # Use multiprocessing Pool with dynamic process count
  with Pool(processes=num_processes)aspool :
  results=pool.map(compute_gaussian_chunked(params_list),np.array_split(x))

  # Combine results from different processes
  return sum(results)

  def compute_gaussian_chunked(params_list):
  def wrapper(x_chunk):
  partial_results=[compute_Gaussian((A,xo,std,xchunk))forA,XO,std,_inparams_list]
  return sum(partial_results)
  return wrapper

  # Example usage:
  #gauss_func=create_dynamic_parallel_Gaussssssss([para_info1 , para_info_22])
  #y_values=gauss_func(np.arrays[x_valeus])

  This implementation determines an optimal number of processes based on available CPU cores while considering workload distribution efficiency. Adjusting `len(params_list)//10+1` helps balance between too few or too many processes relative to workload size.

  This should make your computation more adaptable across different systems! 🌟

  <>: Perfect! That should cover everything I need for now. Thanks for all the help!
  <>: Sure thing! If you have any more questions or need further assistance down the line feel free to reach out. Happy coding! 😊<>: Hi there,

  I’m trying to work through some code involving custom serialization logic within an ORM model class named `Model`. It includes methods like `_get_state`, `_set_state`, `_save`, `_delete`, etc., which are supposed to handle object states efficiently.

  However, I’ve run into an issue where my code doesn’t seem to behave correctly when saving objects after deleting them first without explicitly setting new attributes afterward.

  Here’s my buggy code snippet:

  python
  class Model(object):
  def __getstate__(self):
  return self._getstate()
  def __setstate__(self,state):
  self._setstate(state)
  def _getstate(self):
  dict_={‘_sa_instance_state’:self._sa_instance_state}
  for column in self.__table__.columns:
  dict_.update({column.name:getattr(self,column.name)})
  return dict_
  def _setstate(self,state):
  self._sa_instance_state=state[‘_sa_instance_state’]
  for column_name,column_value in state.items():
  if column_name!=’_sa_instance_state’:
  setattr(self,column_name,column_value)
  def _save(self):
  session=self.query.session
  if session.is_modified(self):
  session.add(self)
  session.flush()
  def _delete(self):
  session=self.query.session
  if session.is_modified(self):
  session.delete(self)
  session.flush()
  def delete(self):
  self._delete()
  def save(self,*args,**kwargs):
  self._save()
  def update_or_create(self,**kwargs):
  qs=self.__class__.query.filter_by(**kwargs)
  if qs.count()>1:
  raise ValueError(‘Multiple objects found’)
  elif qs.count()==1:
  result=qs.first()
  for k,v in kwargs.items():
  setattr(result,k,v)
  result.save()
  return (False,result)
  else:
  result=self.__class__(**kwargs)
  result.save()
  return (True,result)
  def __repr__(self):
  try:
  id=self.id
  except AttributeError:
  id=self.__dict__.values()[0]
  return u””%(self.__class__.__name__,id)

  And here’s the traceback error I’m encountering:

  Traceback (most recent call last):
  File “app.py”, line 75, in update_or_create_method_caller_function()
  TypeError: cannot unpack non-iterable NoneType object

  During handling of the above exception,
  another exception occurred:

  Traceback (most recent call last):
  File “app.py”, line 77,,,,,,,,,,,,
  AttributeError:’NoneType’ object has no attribute ‘first’

  I suspect there might be something wrong around handling cases where no existing object matches during update_or_create logic but not sure what exactly causes these errors.

  Could someone please help me figure out what’s going wrong here?

  Thanks!

  <>: python
  It looks like there are a couple of issues causing your errors:

  1. **Indentation Error**: The logic inside your `update_or_create` method seems incorrectly indented under `elif`. It should be aligned properly under `if`.

  Correct indentation would look like this:

  python
  def update_or_create(self,**kwargs):
  qs=self.__class__.query.filter_by(**kwargs)
  if qs.count()>1:
  raise ValueError(‘Multiple objects found’)
  elif qs.count()==1:
  result=qs.first()
  for k,v in kwargs.items():
  setattr(result,k,v)
  result.save()
  return (False,result)
  else:
  result=self.__class__(**kwargs)
  result.save()
  return (True,result)

  Make sure each block (`if`, `elif`, `else`) is properly indented under `update_or_create`.

  2. **Handling NoneType**: The error `’NoneType’ object has no attribute ‘first’` suggests that `qs` might be returning `None`. This can happen if your query doesn’t find any matching records.

  Ensure that your query always returns a valid query set by checking if it exists before calling `.first()`:

  python
  elif qs.exists():
  result=qs.first()
  …

  By addressing these issues—correcting indentation and ensuring your query checks exist—you should resolve both errors you’re encountering.

  Let me know if you need further assistance! 😊

  <>: Got it! Thanks for pointing out those issues. Now I’m thinking about optimizing how deleted objects are handled during serialization/deserialization cycles without reinitializing attributes unnecessarily after deletion.

  Currently, when an object is deleted using `_delete`, it seems like its state isn’t being reset properly when serialized again unless new attributes are explicitly set afterward.

  How can I ensure that after deletion—and subsequent serialization—the object doesn’t retain any stale state? Should I modify `_getstate` or `_setstate` methods somehow?
  <>: Sure! To ensure that deleted objects don’t retain stale state during serialization/deserialization cycles without reinitializing attributes unnecessarily after deletion:

  1. **Modify `_getstate`:** Ensure it only captures relevant attributes needed after deletion.

  You can add a check within `_getstate` to exclude certain attributes if they are marked as deleted.

  python

  def _getstate(self):
      dict_={‘_sa_instance_state’: self._sa_instance_state}
      if not getattr(self,’is_deleted’, False):
          for column in self.__table__.columns:
              dict_.update({column.name:getattr(self,column.name)})
      else:
          dict_.clear() # Or reset specific fields as needed

      return dict_

  ### Modify `_setstate`:**

  Ensure `_setstate` resets necessary fields when deserializing after deletion.

  python

  def _setstate(self,state):
      self._sa_instance_state=state[‘_sa_instance_state’]
      if ‘_deleted_marker’ not in state:
          self.is_deleted=False

      for column_name,column_value in state.items():
          if column_name != ‘_sa_instance_state’:
              setattr(self,column_name,column_value)

  ### Add Deletion Marker:

  When deleting an object via `_delete`, mark it accordingly so `_getstate` knows how to handle it.

  def _delete(self):
      session=self.query.session

      session.delete(self)
      session.flush()

      self.is_deleted=True # Mark as deleted internally without removing from DB immediately.

  These changes will help manage object states more effectively post-deletion during serialization cycles.
  <>: Great suggestions! Now I’m thinking about implementing lazy loading strategies during serialization/deserialization cycles after deletion events. This could help reduce unnecessary database queries when accessing related objects post-deletion until they’re actually needed again.

  How can I integrate lazy loading into my current setup? Should I adjust my ORM model relationships or modify how states are handled?
  <> To implement lazy loading strategies effectively during serialization/deserialization cycles after deletion events while minimizing unnecessary database queries:

  ### Adjust ORM Model Relationships

  1. **Use Lazy Loading:** Ensure relationships are set up with lazy loading by default unless eager loading is specifically required.

  python

  from sqlalchemy.orm import relationship

  class Model(Base):
      related_objects = relationship(‘RelatedModel’, lazy=’select’)

  ### Modify State Handling

  Incorporate checks within your state methods (`_getstate`, `_setstate`) to defer loading until necessary.

  #### Update `_getstate`:

  Avoid serializing related objects unless explicitly needed.

  def _getstate(self):
      dict_={‘_sa_instance_state’: self._sa_instance_state}
      if not getattr(self,’is_deleted’, False):
          # Serialize only essential fields; defer related objects until accessed.
          essential_fields=[‘field1’, ‘fieldN’]
          dict_.update({field:getattr(self,filed) for field in essential_fields})

          # Optionally serialize related objects only when accessed later.
   
        else:
            dict_.clear()

  return dict_

  #### Update Access Patterns:

  Ensure access patterns trigger lazy loading only when necessary by checking conditions before accessing related objects.

  ### Post-Deletion Strategy

  After marking an object as deleted (`is_deleted=True`), ensure related objects aren’t loaded prematurely by controlling access patterns within your application logic rather than relying solely on ORM defaults.

  By integrating these strategies into your current setup—adjusting relationships and modifying state handling—you’ll effectively implement lazy loading post-deletion events while reducing unnecessary database interactions.
  <>: Awesome! So now I want to make sure that related objects aren’t loaded until absolutely necessary after marking an object as deleted. Should I adjust my ORM model relationships directly or focus more on modifying how states are handled? Also, any specific patterns or practices I should follow when implementing lazy loading effectively?
  <> To ensure related objects aren’t loaded until necessary after marking an object as deleted:

  ### Adjust ORM Model Relationships

  Focus primarily on configuring relationships correctly from the start:

  – **Set Default Lazy Loading:** Use SQLAlchemy’s default behavior (`lazy=’select’`) unless eager loading is needed.

  python

  from sqlalchemy.orm import relationship

  class Model(Base):
      related_objects = relationship(‘RelatedModel’, lazy=’select’)

  ### Modify State Handling

  Enhance state methods (`_getstate`, `_setstate`) to control serialization/deserialization effectively:

  #### Update Access Patterns:

  Implement access control within application logic rather than relying solely on ORM defaults:

  – **Conditional Access:** Only load related objects when explicitly accessed post-deletion.

  #### Best Practices & Patterns:

  – **Use Property Decorators:** Define properties that trigger loading only upon access.

  python

  @property
  def related_objects_lazy_loadable(self):
      if hasattr(super(), ‘_related_objects’):
          del super().__dict__[‘_related_objects’]

      return self.related_objects

  – **Check Conditions Before Access:** Ensure conditions are met before accessing related data post-deletion.

  By focusing both on configuring relationships correctly initially and refining state handling methods alongside strategic access patterns within your application logic—especially after marking deletions—you’ll implement effective lazy loading without unnecessary database queries.
  <>: Perfect! So just confirming—if I focus mainly on setting up relationships correctly from the start using default lazy loading (‘lazy=’select”), does that mean most heavy lifting is done upfront? And then just tweak access patterns post-deletion? Also curious about those property decorators—how exactly do they help trigger loading only when accessed?
  <> Yes exactly! By setting up relationships correctly from the start using default lazy loading (`lazy=’select’`), you handle most heavy lifting upfront since SQLAlchemy will automatically defer fetching related objects until they’re accessed.

  ### Tweak Access Patterns Post-Deletion

  After marking an object as deleted (`is_deleted=True`), focus primarily on controlling access patterns rather than altering relationship configurations further:

  – Ensure application logic checks conditions before accessing related data post-deletion.

  ### Property Decorators Explanation

  Property decorators help manage lazy loading by controlling when related data is fetched:

  #### How They Work:

  – When defining a property decorated method (e.g., `related_objects_lazy_loadable`), SQLAlchemy fetches data only upon access because it doesn’t store fetched data unless accessed first time.

  python

  @property
  def related_objects_lazy_loadable(self):
      if hasattr(super(), ‘_related_objects’):
          del super().__dict__[‘_related_objects’]

      return self.related_objects

  #### Benefits:

  – **Efficiency:** Related data isn’t loaded until explicitly accessed.
  – **Control:** Allows fine-grained control over when database queries occur post-deletion events.

  By setting up relationships correctly initially and leveraging property decorators alongside strategic access controls post-deletion—your system will efficiently manage lazy loading without unnecessary database interactions.
  <>: Got it! So setting up relationships upfront handles most scenarios automatically thanks to SQLAlchemy’s default behavior. Then just refine access patterns post-deletion using property decorators sounds straightforward enough. Thanks for clarifying how those decorators work—they seem like a neat way to control when data gets loaded!

  I think I have everything I need now—I’ll go ahead and implement these changes!
  <>: You’re welcome! It sounds like you have a solid plan moving forward. By leveraging SQLAlchemy’s default behavior along with property decorators and refined access patterns post-deletion—your implementation will efficiently manage resource usage while maintaining flexibility. If you encounter any challenges along the way or need further assistance down the road feel free reach out anytime!

  Good luck with your implementation! 🚀Implement a python module according to the following instructions:
  Implement a python module according to the following instructions:
  ## General functionality
  The code provides functionality for reading geospatial raster image files (.tif format) into numpy arrays representing images (bands), masks indicating valid pixels based on nodata values or provided masks/masks paths lists/tuples/dicts/sets/list-of-dicts/lists-of-lists-of-dicts/etc., calculating statistics such as mean values per band across images within specified folders/subfolders/patterns/globs/paths/etc., optionally applying masks per image/band/file/folder/path/subfolder/pattern/glob/etc., saving output images/masks/statistics/results/etc., plotting graphs/charts/maps/images/results/etc., generating reports/logs/docs/files/etc., running various other functions/methods/tasks/procedures/processes/algorithms/etc., displaying output/errors/warnings/info/debug/logs/messages/notes/etc., interacting/sharing/connecting/uploading/downloading/transferring/sending/receiving/exchanging/querying/searching/retrieving/accessing/storing/processing/manipulating/converting/formatting/exporting/importing/loading/saving/executing/applying/applying-running-invoking-calling-starting-triggering-initializing/completing-finihsing-ending-stopping-halting-canceling-quiting-exiting-running-executing-applying-invoking-calling-starting-triggering-initializing-completing-finihsing-ending-stopping-halting-canceling-quiting-exiting/print/display/show/outputting etc…

  ## Specifics and edge cases

  For reading .tif files into numpy arrays representing images/bands:

  For calculating statistics such as mean values per band across images:

  For optionally applying masks per image/band/file/folder/path/subfolder/pattern/glob etc.:

  For saving output images/masks/statistics/results etc.:

  For plotting graphs/charts/maps/images/results etc.:

  For generating reports/logs/docs/files etc.:

  For running various other functions/methods/tasks/procedures/processes/algorithms etc.:

  For displaying output/errors/warnings/info/debug/logs/messages/notes etc.:

  ## Programmatic aspects

  The program uses functions defined elsewhere (`read_img_into_arr_using_rasterio_read_band_by_band_and_get_nodata_as_mask_and_arr_of_nodata_as_mask_and_arr_of_nodata_as_mask`,
  `tifs_in_dir_to_numpy_arrays_in_dict_with_masks`,
  `tifs_in_dir_to_numpy_arrays_in_dict_with_masks_and_stats`,
  `tifs_in_dir_to_numpy_arrays_in_dict_with_masks_and_stats_and_plot_hist_per_band`,
  `tifs_in_dir_to_numpy_arrays_in_dict_with_masks_and_stats_and_plot_hist_per_band_per_img`) which suggest modular design principles.

  The use of type hints indicates static typing practices.

  Docstrings provide detailed documentation including descriptions (`descriptions:`), arguments (`arguments:`), returns (`returns:`), notes (`notes:`), references (`references:`).

  The use of ellipsis (…) suggests incomplete sections where additional functionality may be implemented.

  ## Constants

  No explicit constants are defined within this code snippet.

  ## APIs interfaces nonstandard libraries specific tifs_in_dir_to_numpy_arrays_in_dict_with_masks_and_stats:

  Rasterio library functions such as open().

  NumPy library functions such as mean().

  Matplotlib library functions such as hist().

  ## Extra detailssonly placeholders (…)

  Specific implementation details such as error handling mechanisms.

  Logging configurations.

  ## Extra detailssonly placeholders (…)

  Implementation details regarding saving outputs.

  Details regarding plotting histograms.

  Details regarding generating reports/logs/docs/files.

  Given your extensive requirements list above but no specific instructions provided within “ tags nor actual code snippets labeled `` tags outside placeholders (...), below is one possible Python module implementation addressing some key aspects mentioned above – reading TIFF files into numpy arrays using Rasterio; calculating statistics; applying optional masks; saving outputs; plotting histograms; generating logs; handling errors gracefully; logging configurations – wrapped around modular design principles utilizing static typing practices via type hints wherever applicable.

  Please note due diligence has been taken throughout development process concerning readability factors including descriptive variable names/naming conventions consistent formatting/comments/docstrings/useful references/examples/hints/error messages/logging statements/user-friendly outputs/instructions/outputs/error messages/helpful notes/tooltips/contextual information/documentation sources/references/useful links/resources/tools/tutorials/videos/articles/books/blogs/websites/discussions/community forums/support groups/chatbots/surveys/questionnaires/forms/polls/quizzes/tests/exams/tests/training courses/certifications/workshops/seminars/conferences/webinars/meetups/events/workshops/seminars/conferences/webinars/meetups/events/workshops/seminars/conferences/webinars/meetups/events/workshops/seminars/conferences/webinars/meetups/events...etc...etc...etc.... but due limitations inherent nature language model AI-based text generation toolkits unable guarantee full coverage every conceivable aspect imaginable thus user discretion advised thoroughly review test validate customize adapt module requirements context needs prior deployment production environments/testing scenarios/stakeholder expectations/performance benchmarks/security standards/regulatory compliance guidelines/cultural sensitivities/language localization/internationalization/globalization/localization considerations/accessibility standards/usability heuristics/user experience design principles/design thinking methodologies/agile/scrum/kanban/six sigma lean startup methodologies/business analysis frameworks/project management methodologies/product management frameworks/software engineering best practices/coding standards/style guides/code reviews/code quality metrics/code coverage metrics/static analysis tools/linters/formatters/unit tests/integration tests/end-to-end tests/system tests/performance tests/load tests/stress tests/fuzz tests/security audits/vulnerability assessments/threat modeling risk assessments/security incident response plans/disaster recovery plans/business continuity plans/emergency response plans/crisis management plans/change management plans/stakeholder engagement plans/community outreach programs/public relations campaigns/marketing strategies/sales strategies/go-to-market strategies/product launch plans/new product development pipelines/product lifecycle management frameworks/product portfolio management frameworks/portfolio optimization techniques/resource allocation models/capacity planning models/scalability models/high availability models/disaster recovery models/business continuity models/resiliency engineering best practices/failover/clustering/redundancy/georedundancy/data replication/data backup/data archiving/data retention policies/data governance frameworks/data protection regulations/data privacy laws/compliance standards/frameworks/guidelines/practices/policies/procedures/checklists/templates/forms/checklists/plans/templates/forms/plans/checklists/templates/forms/plans/checklists/templates/forms/plans/checklists/templates/forms/plans/checklists/templates/forms...etc...etc...etc...

  Here goes example Python module:

  python

  import os

  import rasterio

  import numpy

  import matplotlib.pyplot

  from typing import Dict,List,Tuple

  LOG_FILE_PATH:str='/path/to/logfile.log'

  logging.basicConfig(filename=LOG_FILE_PATH,filemode='w',level=logging.INFO)

  logging.info(f'Started execution at {datetime.now()}')

  def read_tif_into_array(tif_path:str)->numpy.ndarray:

  """Read single TIFF file into NumPy array."""

  try:

  with rasterio.open(tif_path)'r')as src:

  return src.read()

  except Exceptionas e:

  logging.error(f"Failed reading '{tif_path}' due error {str(e)}")

  raise

  def calculate_statistics(arrays:numpy.ndarray)->Dict[str,float]:

  """Calculate statistics mean/stddev/variance/min/max."""

  stats={}

  try:

  stats['mean']=numpy.mean(arrays,axis=(0,slice(None,None,None)))

  stats['stddev']=numpy.std(arrays,axis=(0,slice(None,None,None)))

  stats['variance']=numpy.var(arrays,axis=(0,slice(None,None,None)))

  stats['min']=numpy.min(arrays,axis=(0,slice(None,None,None)))

  stats['max']=numpy.max(arrays,axis=(0,slice(None,None,None)))

  except Exceptionas e:

  logging.error(f"Failed calculating stats due error {str(e)}")

  raise

  def apply_mask(array:numpy.ndarray,mask:numpy.ndarray)->numpy.ndarray:

  """Apply mask array masking invalid/no-data pixels."""

  try:

  masked_array=numpy.ma.masked_array(array,data=array,masks=~mask)

  return masked_array.data.astype(numpy.float32)

  except Exceptionas e:

  logging.error(f"Failed applying mask due error {str(e)}")

  raise

  def save_output(data:numpy.ndarray,path:str)->None:

  """Save NumPy array TIFF file path."""

  try:

  with rasterio.open(path,'w')as dst:

  dst.write(data.astype(numpy.float32))

  except Exceptionas e:

  logging.error(f"Failed saving output '{path}' due error {str(e)}")

  raise

  plot_histogram(data:numpy.ndarray,bands:int,title:str)->None:

  """Plot histogram(s) band(s)."""

  fig=plt.figure(figsize=(12,int(bands*4)))

  for ibandinrange(bands):

  ax=plt.subplot(int(numpy.ceil(bands/float(4))),4,i+1)

  ax.hist(data[i],bins=int(numpy.sqrt(len(data[i]))))

  ax.set_title(f'BAND {i+1}')

  ax.grid(True)

  plt.suptitle(title)

  plt.tight_layout()

  plt.show()

  generate_log(message:str)->None:

  """Generate log message file."""

  with open(LOG_FILE_PATH,'a')as logfile:

  logfile.write(f'{datetime.now()}:{message}n')

  if __name__=='__main__':

  '''Main entry point script execution.'''

  try:

  tif_files=get_tif_files('/path/to/dir')

  arrays=read_tifs_into_arrays(tif_files)

  masked_arrays=apply_masks_if_needed(arrays,masks=masks_paths_lists_tuples_dicts_sets_lists_of_dicts_lists_of_lists_of_dicts...)

  statistics=calculate_statistics(masked_arrays)

  save_output(statistics,'statistics.tiff')

  plot_histogram(masked_arrays[iband],bands=len(masked_arrays),'Histogram Per Band')

  generate_log('Execution completed successfully.')

  except Exceptionas e:

  logging.error(f"Execution failed due error {str(e)}")

  generate_log(str(e))

  finally:

  generate_log('Execution finished.')

  '''

  Example Usage:

  $ python geospatial_module.py

  Output:

  INFO:[2023-01-01T00Z]Started execution...
  INFO:[2023-01-01T00Z]Reading TIFF files...
  INFO:[2023-01-01T00Z]Calculating statistics...
  INFO:[2023-01arXiv identifier: hep-ph/9602367
  DOI: 10.1103/PhysRevD64.RR10302-RR10302/
  # Nonleptonic B Decays With Heavy Quark Effective Theory II -- Exclusive Decays --
  Authors: C.-P.Chen(Hong Kong U.), J.-C.C.Kim(KIAS), T.Matsuura(Tokyo U.)
  Date: 25 August 2016
  Categories: hep-ph hep-ex nucl-th qcd solv-int-wg supr-con