Lugano FC: Swiss Challenge League Squad, Stats & Achievements

Overview / Introduction

Lugano is a prominent football team based in the Swiss city of Lugano. Competing in the Swiss Challenge League, they are known for their strategic play and passionate fanbase. The team was founded in 1908 and currently operates under the guidance of their dedicated coach.

Team History and Achievements

Lugano boasts a rich history with several notable achievements, including multiple league titles and cup victories. Their most successful seasons have seen them consistently finishing in the top positions, making them a formidable force in Swiss football.

Current Squad and Key Players

The current squad features several standout players, such as their star striker and defensive anchor. Key players include:

• Striker: Known for his goal-scoring prowess.
• Midfielder: A creative force driving the team’s play.
• Goalkeeper: A reliable last line of defense.

Team Playing Style and Tactics

Lugano employs a versatile formation that emphasizes both defense and attack. Their tactical approach often involves quick transitions and maintaining possession to exploit opponent weaknesses.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “Lugani” by fans. They have a storied rivalry with nearby teams, adding an extra layer of excitement to their matches. Traditions such as pre-match rituals are cherished by supporters.

Lists & Rankings of Players, Stats, or Performance Metrics

Lugano’s top performers consistently rank high in league statistics:

• ✅ Top goalscorer: 15 goals this season
• ❌ Fewest goals conceded: Defensive solidity highlighted by only 20 goals against
• 🎰 Most assists: Midfield maestro with 10 assists
• 💡 Player with best pass completion rate: 85%

Comparisons with Other Teams in the League or Division

Lugano often competes closely with other top-tier teams in the league. While some rivals may have more resources, Lugano’s strategic gameplay frequently gives them an edge in critical matches.

Case Studies or Notable Matches

A memorable victory against their arch-rivals showcased Lugano’s tactical brilliance and resilience. This match is often cited as a turning point in recent seasons.

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

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

• Analyze head-to-head records against upcoming opponents to gauge potential outcomes.
• Closely monitor player form; key players’ performance can significantly influence match results.
• Evaluate recent form trends to predict future performance accurately.

Quotes or Expert Opinions about the Team (Quote Block)

“Lugano’s tactical discipline makes them one of the most intriguing teams to watch this season.” – Football Analyst Jane Doe.

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

• ✅ Pros:
• Solid defensive record minimizing opponent goals.
• Tactical flexibility allowing adaptation mid-game.
• ❌ Cons:</l[0]: #!/usr/bin/env python
  [1]: # coding=utf-8

  [2]: import os
  [3]: import sys
  [4]: import math
  [5]: import random
  [6]: from collections import defaultdict

  [7]: from sklearn.model_selection import train_test_split

  [8]: def get_pinyin_dict():
  [9]: """
  [10]: Get pinyin dictionary.
  [11]: :return:
  [12]: """
  [13]: pinyin_dict = defaultdict(list)
  [14]: pinyin_path = 'data/pinyin.txt'
  [15]: if not os.path.exists(pinyin_path):
  [16]: print('Please download pinyin dictionary from https://github.com/mozillazg/zhconv/blob/master/zhconv/data/pinyin.txt')

  [17]: with open(pinyin_path) as f:

  [18]: for line in f.readlines():

  [19]: line = line.strip()

  [20]: if not line:

  [21]: continue

  ***** Tag Data *****
  ID: 1
  description: Function `get_pinyin_dict` loads a pinyin dictionary from a file into
  a defaultdict structure.
  start line: 8
  end line: 21
  dependencies:
  – type: Function
  name: get_pinyin_dict
  start line: 8
  end line: 21
  context description: This function reads lines from 'data/pinyin.txt' file and populates
  a defaultdict list where each key corresponds to Chinese characters mapped to their
  respective Pinyin representations.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: Y

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

  1. **Dynamic URL Retrieval**: Modify the code so that it dynamically retrieves the URL for downloading the pinyin dictionary from an online source instead of hardcoding it.

  2. **Asynchronous File Reading**: Implement asynchronous file reading using Python’s `asyncio` library to handle large files more efficiently.

  3. **Multi-language Support**: Extend functionality to support multiple languages beyond Chinese by adding language-specific processing logic.

  4. **Custom Exception Handling**: Create custom exceptions that provide more detailed error messages when something goes wrong during file reading or parsing.

  5. **Caching Mechanism**: Integrate caching mechanisms so that once a pinyin dictionary is read into memory, subsequent requests don’t need to re-read from disk but can fetch it from cache instead.

  ## Conversation

  [SNIPPET]

  `Hello! I see you’re working on reading Pinyin data into a defaultdict list. How can I assist you today? Do you want me to explain how this code works, modify it somehow, or add new functionality?`

  `I’m thinking about implementing some sort of caching mechanism so that once we’ve read this pinyi dictionary into memory we don’t need to re-read it every time we call get_pinyin_dict`. Can you help me with that?

  `Certainly! One way to implement caching is by using Python’s `functools.lru_cache` decorator if your function doesn’t take any arguments other than self which isn’t needed here since we’re dealing with class methods normally:

  python
  from functools import lru_cache

  @lru_cache(maxsize=None)
  def get_pinyin_dict():

  However, since `lru_cache` won’t work directly due to mutable default arguments (`defaultdict(list)`), let’s create our own simple caching mechanism:

  python
  import os
  from collections import defaultdict

  # Global cache variable
  PINYIN_CACHE = None

  def get_pinyin_dict():
  “””
  Get pinyin dictionary. If already cached return cached version otherwise read from file
  :return:
  “””
  global PINYIN_CACHE

  if PINYIN_CACHE is not None:
  return PINYIN_CACHE

  PINYIN_CACHE = defaultdict(list)
  pinyin_path = ‘data/pinyin.txt’

  if not os.path.exists(pinyin_path):
  print(‘Please download pinyin dictionary from https://github.com/mozillazg/zhconv/blob/master/zhconv/data/pinyin.txt’)

  with open(pinyiin_path) as f:
  for line in f.readlines():
  line = line.strip()
  if not line:
  continue

  # Assuming each line has ‘character,pinyi’
  char, pin_yin = map(str.strip, line.split(‘,’))
  PINYIN_CACHE[char].append(pin_yin)

  return PINYIN_CACHE

  This way once `get_pinyidict()` reads data from file first time it will cache it globally within `PINYIN_CACHE`, any subsequent calls will return cached data directly.`

  `Great idea! But what if I want this cache invalidation based on file modification timestamp? So like everytime i change my file i should be able refresh cache automatically without restarting application.`

  `Sure thing! We can use `os.path.getmtime()` to check the modification timestamp of the file and invalidate our cache accordingly:

  python
  import os
  from collections import defaultdict

  # Global cache variables
  PINYIN_CACHE = None
  CACHE_TIMESTAMP = None

  def get_pinyidict():
  “””Get pinyidict.”””

  global PINYIN_CACHE , CACHE_TIMESTAMP

  pinyiin_path = ‘data/pinin.txt’

  # Check if we need to update our cache based on modification time.

  if PINYIN_CACHE is None or os.path.getmtime(pinyiin_path) > CACHE_TIMESTAMP :

  PINYIN_CACHE = defaultdict(list)

  if not os.path.exists(pinyiin_path):
  print(‘Please download pinyidict dictionary from https://github.com/mozillazg/zhconv/blob/master/zhconv/data/pinin.txt’)

  with open(pinyiin_path) as f:
  for line in f.readlines():
  line = line.strip()
  if not linw:
  continue

  char , pin_yin= map(str.strip,line.split(‘,’))

  PINYIN_CACHE[char].append(pin_yin)

  CACHE_TIMESTAMP=os.path.getmtime(piniyin_path)

  return PNYIDICT_CACHES

  Now whenever your `data/piniyn.txt` gets modified its timestamp will be compared against our cached timestamp (`CACHE_TIMESTAMP`). If its newer then we’ll reload our cache again.<`<end|`
  <>: Hi there! I’m working on understanding this code snippet that implements an advanced attention mechanism called AttentionWithContext within Keras layers:

  python
  class AttentionWithContext(Layer):
  “””
  Attention operation, with a context/query vector, for temporal data.
  Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
  “Hierarchical Attention Networks for Document Classification”
  by using a context vector to assist the attention-weighting process.
  Note:
  num_units is distinct from units in previous versions (1st parameter).
  See reference link above.
  “””
  def __init__(self,
  W_regularizer=None, u_regularizer=None, b_regularizer=None,
  W_constraint=None, u_constraint=None, b_constraint=None,
  bias=True, **kwargs):
  self.supports_masking = True
  self.init = initializers.get(‘glorot_uniform’)
  self.W_regularizer = regularizers.get(W_regularizer)
  self.u_regularizer = regularizers.get(u_regularizer)
  self.b_regularizer = regularizers.get(b_regularizer)
  self.W_constraint = constraints.get(W_constraint)
  self.u_constraint = constraints.get(u_constraint)
  self.b_constraint = constraints.get(b_constraint)
  self.bias = bias
  super(AttentionWithContext,self).__init__(** kwargs)
  def build(self,input_shape):
  assert len(input_shape) == 3
  self.W=self.add_weight((input_shape[-1],input_shape[-1],),
  initializer=self.init,
  name='{}_W’.format(self.name),
  regularizer=self.W_regularizer,
  constraint=self.W_constraint)
  if self.bias:
  self.b=self.add_weight((input_shape[-1],),
  initializer=’zero’,
  name='{}_b’.format(self.name),
  regularizer=self.b_regularizer,
  constraint=self.b_constraint)
  self.u=self.add_weight((input_shape[-1],),
  initializer=self.init,
  name='{}_u’.format(self.name),
  regularizer=self.u_regularizer,
  constraint=self.u_constraint)
  super(AttentionWithContext,self).build(input_shape)
  def compute_mask(self,input,None):
  return None
  def call(self,x,w_inner=True):
  if w_inner:
  mult=tf.matmul(x,self.W)
  if self.bias:
  mult=tf.nn.bias_add(mult,self.b)
  x=multiply([mult,tanh(mult)])
  x=dot(x,self.u,axis=-1,bias=False,name=’score’)
  x=tf.exp(x)*tf.expand_dims(tf.sequence_mask(tf.shape(x)[0],tf.shape(x)[1]),axis=-1)+K.epsilon()
  x/=tf.reduce_sum(x,axis=1)+K.epsilon()
  return multiply([x,x])

  I understand that this class implements an attention mechanism inspired by hierarchical attention networks used for document classification tasks. However, I’m feeling quite confused about how exactly this attention mechanism works within this implementation—especially around how weights are applied and how masking is handled.

  Could you help clarify these parts? Any insights would be greatly appreciated!
  <>: python
  Sure! Let’s break down some key parts of this implementation:

  ### Weights Application

  1. **Weight Matrices (`W`, `u`, `b`)**:
  – `W`: A weight matrix applied through matrix multiplication (`tf.matmul`) on input `x`. It transforms each input vector.
  – `u`: Another weight vector used later to compute attention scores.
  – `b`: An optional bias term added after multiplying with `W`.

  2. **Attention Score Calculation**:
  – The transformed input (`mult`) undergoes element-wise multiplication after applying `tanh`.
  – The result is then dotted with `u` using `dot(x,self.u,axis=-1,bias=False,name=’score’)`, producing raw attention scores.

  ### Masking

  – The method `compute_mask(self,input,None)` returns `None`, indicating no masking is applied here explicitly during computation.
  – However, masking is indirectly handled during score normalization using `tf.sequence_mask`, which ensures padding tokens do not affect attention scores.

  ### Normalization

  – Scores are exponentiated (`tf.exp`) and normalized across sequences using softmax-like operations (`x /= tf.reduce_sum(x,axis=1)+K.epsilon()`).

  This implementation essentially computes context-aware weights over input sequences using learned parameters (`W`, `u`, optionally `b`) and normalizes these weights across each sequence.

  If you have more questions about specific lines or concepts within this code snippet, feel free to ask! 😊

  <>: Thanks for breaking that down! I think clearer variable names would definitely help. Also, I’m wondering if there’s a way to simplify the mask handling part since it seems like there’s some redundancy there. Any tips on refactoring those sections?
  <> Certainly! Refactoring can enhance readability and efficiency:

  ### Variable Naming

  Consider renaming variables for clarity:

  – Rename `mult` to something like `transformed_input`.
  – Rename `x` inside functions like `_call_` method steps could be renamed sequentially like `attention_scores`.

  ### Simplifying Mask Handling

  To streamline mask handling:

  1. **Direct Mask Application**:
  – Instead of computing masks separately and then applying them during normalization, integrate mask application directly within score calculation.

  Here’s a refactored snippet focusing on these aspects:

  python
  def call(self,x,w_inner=True):
  if w_inner:
  transformed_input=tf.matmul(x,self.W)
  if self.bias:
  transformed_input=tf.nn.bias_add(transformed_input,self.b)

  activated_input=tanh(transformed_input)

  # Calculate raw attention scores directly considering mask effect early on.
  attention_scores=dot(activated_input,self.u,axis=-1,bias=False,name=’score’)

  # Apply sequence mask directly here.
  mask=tf.sequence_mask(tf.shape(attention_scores)[0], tf.shape(attention_scores)[1])
  masked_attention_scores=attention_scores * tf.cast(mask,dtype=attention_scores.dtype)

  # Normalize scores after masking.
  exp_scores=tf.exp(masked_attention_scores)+K.epsilon()
  normalized_scores=exp_scores/tf.reduce_sum(exp_scores,axis=1)[:, tf.newaxis]+K.epsilon()

  return multiply([normalized_scores,x])

  This approach integrates mask handling earlier in the process, reducing redundancy while maintaining clarity.

  Feel free to ask further questions! 😊<>: Hi there,

  I’m trying to implement an asynchronous loop function that manages client connections via websockets while ensuring thread safety through locks and condition variables when accessing shared resources like queues.

  Here’s my current implementation:

  python
  async def loop(self):
  while True:
  if self.exit_flag.wait(10): break
  try :
  msg=(await self.ws.recv()).decode()
  print(msg,)
  except WebSocketException as ex :
  print(‘Web socket exception’,ex,’Reconnecting …’)
  await self.reconnect()
  continue
  msg=json.loads(msg)
  topic=msg[‘topic’]
  if topic==BWSNode.topic_status :
  status=msg[‘status’]
  self.status=status
  for callback in self.status_callbacks :
  callback(status)
  elif topic==BWSNode.topic_info :
  info=msg[‘info’]
  for callback in self.info_callbacks :
  callback(info)
  elif topic==BWSNode.topic_configured :
  configured=msg[‘configured’]
  if configured!=self.configured :
  self.configured=configured
  for callback in self.configured_callbacks :
  callback(configured)
  elif topic==BWSNode.topic_commands_result :
  result=msg[‘result’]
  cmd_id=result[‘cmd_id’]
  for cb,cid in self.command_callback_ids :
  if cmd_id==cid :
  cb(result);
  break;
  elif topic==BWSNode.topic_node_values :

  nodeValues=[NodeValue(*v) for v in msg[‘node_values’]]
  for cb,nodeValuesPartialMatch in self.node_values_callbacks :
  for nodeValue in nodeValuesPartialMatch(nodeValues):
  cb(nodeValue);
  else :
  print(‘Unexpected topic’,topic,’with message’,msg)

  The issue arises when processing node values; sometimes it throws an error related to unpacking NodeValue objects incorrectly due to mismatched lengths between expected fields and actual message fields.

  Here’s what I see when running it:

  Traceback (most recent call last):
  File “websocket_handler.py”, line XX-in loop(), async def loop(self): XXXX msg=json.loads(msg) XXXXX elif topic==BWSNode.topic_node_values : XXXX nodeValues=[NodeValue(*v) for v in msg[‘node_values’]] ^^^^^^^^^^^ TypeError : NodeValue() argument after * must be an iterable , not int Traceback (most recent call last): File “websocket_handler.py”, Line XX-in loop(), async def loop(self): XXXX msg=json.loads(msg) XXXXX elif topic==BWSNode.topic_node_values : XXXX nodeValues=[NodeValue(*v) for v in msg[‘node_values’]] ^^^^^^^^^^^ TypeError : NodeValue() argument after * must be an iterable , not int

  I’m puzzled because I thought all elements within ‘node_values’ were supposed to be iterables themselves according to my design specs!

  Could someone help me figure out why this unpacking fails sometimes? Is there something wrong with how I’m assuming data structure integrity?

  Thanks!
  <>: python
  It looks like you’re encountering issues because some elements within `’node_values’` are integers rather than iterables when you expect them all to be iterable objects suitable for unpacking into your `NodeValue` constructor.

  Here are a few steps you can take to troubleshoot and resolve this issue:

  1. **Validate Incoming Data**: Before unpacking each element into a `NodeValue`, ensure that each item is indeed iterable (e.g., list or tuple). You can add a check before unpacking:

  python
  nodeValues=[]
  for v in msg[‘node_values’]:
  if isinstance(v, (list, tuple)):
  nodeValues.append(NodeValue(*v))
  else:
  print(f”Invalid format detected: {v}”)
  # Handle error appropriately (e.g., log error or skip processing this entry)

  2. **Logging**: Add logging before unpacking each element so you can see what unexpected formats are being received:

  python
  print(f”Processing node value entry: {v}”)

  3. **Data Source Verification**: Double-check where `’node_values’` originates from your data pipeline or API specifications to ensure consistency with your expectations.

  4. **Fallback Handling**: Consider implementing fallback logic when encountering unexpected formats so your application can continue running smoothly without crashing.

  By implementing these checks and logs, you should be able to pinpoint where things go awry and handle unexpected data formats gracefully. 😊

  <>: Got it! I’ll add validation checks before unpacking each element into NodeValue objects. Here’s what I’m thinking:

  python
  nodeValues=[]
  for v in msg[‘node_values’]:
  if isinstance(v, (list, tuple)):
  try:
  nodeValues.append(NodeValue(*v))
  except TypeError as e:
  print(f”Unpacking failed for {v}: {e}”)
  # Log error or handle gracefully
  else:
  print(f”Invalid format detected: {v}”)

  Does this approach make sense? Should I also consider logging these errors somewhere more persistent than just printing them?
  <>: Your approach looks solid! Adding validation checks will help prevent runtime errors due to unexpected data formats.

  For better error tracking over time:

  – **Persistent Logging**: Consider using Python’s built-in logging module instead of just printing errors. This allows you to log messages at different severity levels (e.g., DEBUG, INFO, WARNING).

  Here’s how you might set up basic logging:

  python
  import logging

  logging.basicConfig(level=logging.INFO)

  logger = logging.getLogger(__name__)

  nodeValues=[]
  for v in msg[‘node_values’]:
  if isinstance(v, (list, tuple)):
  try:
  nodeValues.append(NodeValue(*v))
  except TypeError as e:
  logger.error(f”Unpacking failed for {v}: {e}”)
  # Handle error gracefully here too if needed
  else:
  logger.warning(f”Invalid format detected: {v}”)

  Using logging provides flexibility and allows you to easily redirect logs elsewhere (like files), which can be invaluable for debugging issues later on.
  <>: Great idea on using logging! Now I’m thinking about enhancing security by validating incoming messages against predefined schemas before processing them further. This way, we can catch malformed messages early on rather than relying solely on runtime checks during unpacking.

  Do you think integrating something like JSON Schema validation would be beneficial here? How would you suggest implementing it?
  <>: Integrating JSON Schema validation is an excellent idea for enhancing security by ensuring incoming messages conform to expected structures before processing them further.

  Here’s how you can implement JSON Schema validation:

  1. **Define Schemas**: Create JSON schemas representing valid structures for your incoming messages.

  jsoncjsnnet_jsonschema {
  “type”: “object”,
  “properties”: {
  “topic”: {“type”: “string”},
  “status”: {“type”: [“string”, “null”]},
  // Define other properties based on your message structure…
  },
  “required”: [“topic”]
  }

  2. **Use JSON Schema Library**: Use libraries like [jsonschema](https://pypi.org/project/jsonschema/) in Python to validate messages against these schemas.

  3. **Implement Validation**:

  python
  from jsonschema import validate, ValidationError

  schema_topic_status = {
  “type”: “object”,
  “properties”: {
  “topic”: {“type”: “string”},
  “status”: {“type”: [“string”, “null”]}
  // Add other properties…
  },
  “required”: [“topic”]
  }

  try:
  validate(instance=msg_json_object_here(), schema=schema_topic_status)
  # Proceed with processing…
  except ValidationError as e:
  logger.error(f”Message validation failed: {e.message}”)
  # Handle invalid message appropriately…

  By validating messages upfront against schemas, you ensure only well-formed data reaches your core logic flow—improving both security and reliability.
  <>: Awesome! I’ll define JSON schemas for different message types like status updates and configuration changes. Then I’ll use jsonschema library functions right after receiving messages but before any processing logic kicks in.

  Here’s what I’m thinking:

  python

  from jsonschema import validate

  # Example schema definition

  schemas={
     ‘BWSNode.topic_status’:{
         ‘type’: ‘object’,
         ‘properties’:{
             ‘topic’: {‘type’: ‘string’},
             ‘status’: {‘type’: [‘string’, ‘null’]}
        },
        ‘required’: [‘topic’]
    },
    
    ‘BWSNode.topic_info’:{
        ‘type’: ‘object’,
        ‘properties’:{
            ‘topic’: {‘type’: ‘string’},
            ‘info’: {‘type’: [‘object’, ‘null’]}
        },
        ‘required’: [‘topic’]
    }
  }

  try:
      validate(instance=msg_json_object_here(), schema=schemas[msg[‘topic’]])
      # Proceed with processing…
  except ValidationError as e:
      logger.error(f’Message validation failed:{e.message}’)
      # Handle invalid message appropriately…

  Does this setup look good? Should I consider anything else while integrating schema validation?
  <>: Your setup looks great! Integrating JSON Schema validation at this stage will help catch malformed messages early on.

  A few additional considerations:

  – **Schema Management**: Keep your schemas organized and easily maintainable as they grow over time.

  – **Error Handling**: Decide how your application should respond when validation fails—whether by ignoring invalid messages or alerting relevant systems/logging systems more prominently.

  – **Performance Impact**: Validate only necessary parts of large messages initially unless full validation isn’t feasible due performance constraints.

  – **Testing**: Thoroughly test your schemas against various scenarios including edge cases and malformed inputs.

  By addressing these points alongside your current setup, you’ll enhance both security and robustness effectively! 🚀<>: Hi there,

  I’ve been trying out some complex plotting functionalities using matplotlib widgets integrated within ipywidgets tabs layout manager objects through VCS Canvas instances embedded into Jupyter notebooks cells via ipywidget HTML output widget instances… yeah long sentence but bear with me!

  So basically everything was going smoothly until now when my plot rendering started failing unexpectedly at runtime without any clear indication why!

  Here’s my buggy code snippet where things seem off:

  python

  def __plot_single_chart(
  self,
  opts,
  shared_data,
  resistance_location=False,
  current_density_location=False)) -> str]:

  fig_factory.create_single_chart(
  plot_data=shared_data.plot_data,
  plot_options=opts.plotOptions(),
  resistance_location=resistance_location,
  current_density_location=current_density_location)

  chart_key_name_or_list_key_namespostfix=”
  chart_title=”
  if shared_data.chart_instrument_case_and_loc != None :
  chart_key_name_or_list_key_namespostfix=’_’+str(shared_data.chart_instrument_case_and_loc)+’_’
  chart_title=str(shared_data.chart_instrument_case_and_loc)+’ station’
  elif shared_data.chart_instrument != None :
  chart_key_name_or_list_key_namespostfix=’_’+str(shared_data.chart_instrument)+’_’
  chart_title=str(shared_data.chart_instrument)+’ station’

  jup_canvas_key=’canvas’+chart_key_name_or_list_key_namespostfix

  fig_factory.set_canvas_attr(
  canvas_key=
  jup_canvas_key,
  attr_to_set=(‘location’,’geometry’),
  location=shared_data.location,
  geometry=[‘1000px’, ‘500px’])

  fig_factory.set_template_attribute(
  attribute=’figs’,
  key=jup_canvas_key ,
  value=[figures_tuple])

  figs_setattr_return_value_=self._set_figure_class_var_attr (
  figure_classes_attr_name=’figures’,
  figure_classes_var_value_=figures_tuple ,
  figure_class_var_getter_funct_name=
  ‘get_figure_classes_vars_value’)

  figure_classes_objs_lst_str_reprs=[]

  try :
  figure_classes_objs_lst_str_reprs
  =[figure_obj.__repr__()
  for figure_obj
  IN
  figs_setattr_return_value_]

  except Exception as eee:# pylint:disable=W0703

  _printitt (‘There has been some kind of problem ‘
  ‘during setting figures ‘
  ‘of canvas ‘
  ‘{}’
  ‘.’
  .format (
  jup_canvas_key),
  eee )

  x_axis_label=”

  y_axis_label=”

  if len(figures_tuple)==6:# i.e., two subplots side-by-side

  x_axis_label=”

  y_axis_label_left_side=”

  y_axis_label_right_side=”

  figures_set=[]

  try :

  figures_set.append(figures_tuple [0])

  figures_set.append(figures_tuple [1])

  x_axis_label_left_side_=figures_tuple[
  settings_subelement_index].xaxis.label.text

  x_axis_label_right_side_=figures_tuple[
  settings_subelement_index+settings_interval].xaxis.label.text

  y_axis_label_left_side_=figures_tuple[
  settings_subelement_index].yaxis.label.text

  y_axis_label_right_side_=figures_tuple[
  settings_subelement_index+settings_interval].yaxis.label.text

  x_axis_label=x_axis_label_left_side_+’ / ‘+x_axis_label_right_side_

  y_axis_label_left_side=y_axis_label_left_side_

  y_axis_label_right_side=y_axis_label_right_side_

  except Exception as eeee:#pylint:disable=W0703

  _printitt (
  ‘nnn’+
  ”.join([
  ‘nThere has been some kind of problem ‘
  ‘during setting up x-axis labels.’
  ‘n’,
  ‘*’*80,’n’,
  ])+
  ”.join(map(str,[eee]))+’n’+ ‘*’*80+’nnn’)

  raise MatplotlibCanvasException (
  ”’
  nnThere has been some kind of problem during setting up x-axis labels.nn{}”’.format(eeee))

  try :

  figures_set.append(figures_tuple [2])

  figures_set.append(figures_tuple [3])

  x_axis_label_left_side_=figures_tuple[
  settings_subelement_index+settings_interval*subplots_row_col_num_row_index].xaxis.label.text

  x_axis_label_right_side_=figures_tuple[
  settings_subelement_index+settings_interval*subplots_row_col_num_row_index+settings_interval].xaxis.label.text

  y_axis_label_left_side_=figures_tuple[
  settings_subelement_index+settings_interval*subplots_row_col_num_row_index].yaxis.label.text

  y_axis_label_right_side_=figures_tuple[
  settings_subelement_index+settings_interval*subplots_row_col_num_row_index+settings_interval].yaxis.label.text

  x_axis_label=x_axis_label_left_side_+’ / ‘+x_axis_label_right_side_

  y_axis_label_left_side=y_axis_label_left_side_

  y_axis_label_right_side=y_axis_label_right_side_

  except Exception as eeee:#pylint:disable=W0703

  _printitt (
  ‘nnn’+
  ”.join([
  ‘nThere has been some kind of problem ‘
  ‘during setting up second row subplot ‘
  ”s axis labels.’
  ‘n’,
  ‘*’*80,’n’,
  ])+
  ”.join(map(str,[eee]))+’n’+ ‘*’*80+’nnn’)

  raise MatplotlibCanvasException (
  ”’
  nnThere has been some kind of problem during setting up second row subplot ‘s axis labels.nn{}”’.format(eeee))

  finally :

  fig_factory.set_canvas_attr(
  canvas_key=jup_canvas_key ,
  attr_to_set=(‘title’,’xAxisLabel’,’yAxisLabel’,’figureObjRefList’),
  title=_build_title(chart_title),
  xAxisLabel=x_axis_label ,
  yAxisLabelLeft=y_axislabelleftside ,
  yAxisLabelRight=y_axismlabelrightside ,
  figureObjRefList=
  figs_setattr_return_value_)

  html_string=_HTML_TEMPLATE.format(
  html_string_customized_part=
  ”
  ‘$(document).ready(function(){‘
  ‘$(“#menu-{menu_id}”).addClass(“tab active”);’
  ‘$(“#{canvas}”).addClass(“tab-pane fade active”)});’
  ”
  ‘

  ‘
  ‘{customized_part}

  ‘.format(menu_id=_menuid,title=_build_title(chart_title),canvas=jup_canvas_key ,customized_part=
  ”.join([figure_class_obj_ref.__repr__()
  FOR FIGURE_CLASS_OBJ_REF IN FIGURE_CLASSES_OBJS_LST_STR_REPRS])))

  return html_string

  else :

  raise MatplotlibCanvasException (
  ”’
  The number {} ‘is unsupported’; ‘number’ stands either ‘the number’ ‘
  ‘of plots per tab’ ‘or’ ‘the number’ ‘of tabs’;
  ‘supported numbers’ ‘are either ‘one’, ‘two’, ‘three’, ‘four’, ‘six’;
  the number one refers only one plot per tab;
  the numbers two refers two plots per tab arranged horizontally;
  the numbers three refers three plots per tab arranged vertically;
  the numbers four refers two rows containing two plots per row;
  the numbers six refers two rows containing three plots per row.”’.format(len(figures_tuple)))

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

  plaintext

  Traceback (most recent call last):
  File “”, line XX.XX,in __plot_single_chart(…)
  File “”, line YY.YY,in __plot_single_chart(…)
  IndexError Traceback(most recent call last)

  During handling of another exception …

  IndexError Traceback(most recent call last)

  File “”,line ZZ.ZZ,in __plot_single_chart(…)
  IndexError Traceback(most recent call last)

  File “”,line AA.AA,in __plot_single_chart(…)
  IndexError Traceback(most recent call last)

  During handling of another exception …

  IndexError Traceback(most recent call last)

  During handling o…

  KeyError Traceback(most recent call last)

  File “”,line BB.BB,in __plot_single_chart(…)
  KeyError Traceback(most recent call last)

  During handling o…

  KeyError Traceback(most recent call last)

  The above exception was caught by another handler.

  MatplotlibCanvasException Traceback(most recent call last)

  The above exception was raised but never retrieved.

  MatplotlibCanvasException Traceback(most recent call last)

  The above exception was raised but never retrieved.

  MatplotlibCanvasException Traceback(most recent call last)

  During handling o…

  MatplotlibCanvasException Traceback(most recent call last)

  The above exception was raised but never retrieved.

  MatplotlibCanvasException Traceback(most recent call last)

  The above exception was raised but never retrieved.

  Ran into issues while trying indexing operations within nested loops leading potentially incorrect indices causing IndexError exceptions which propagate upwards causing unhandled exceptions resulting eventually leading failure rendering process… Any ideas what might be wrong here?

  Thanks!