Sporting CP: Premier League Portugal Squad & Achievements

Overview of Sporting

Sporting Clube de Portugal, commonly known as Sporting, is a prominent football club based in Lisbon, Portugal. Competing in the Primeira Liga, Sporting has been a staple in Portuguese football since its founding in 1906. Known for their passionate fanbase and rich history, Sporting plays under the management of Rúben Amorim and employs a dynamic 4-3-3 formation.

Team History and Achievements

Sporting has a storied history filled with numerous titles and accolades. They have won the Primeira Liga 19 times, including recent triumphs in 2020-21 and 2022-23 seasons. The club also boasts multiple Taça de Portugal (Portuguese Cup) victories and has made several appearances in European competitions such as the UEFA Champions League.

Current Squad and Key Players

The current squad features standout players like João Palhinha (midfielder), Pedro Gonçalves (striker), and Nuno Santos (winger). These key players are instrumental to Sporting’s tactical setup and performance on the field.

Team Playing Style and Tactics

Sporting employs an aggressive 4-3-3 formation focusing on high pressing and quick transitions. Their strengths lie in their attacking prowess and midfield control, while their weaknesses can be found in defensive consistency against top-tier teams.

Interesting Facts and Unique Traits

Sporting is affectionately known as “Leões” (Lions) due to their lion emblem. The club has a massive fanbase known for their vibrant support during matches. One of Sporting’s fiercest rivalries is with Benfica, often referred to as “O Clássico.”

Lists & Rankings of Players & Performance Metrics

• Top Performers: João Palhinha ✅, Pedro Gonçalves 🎰
• Key Statistics: Average possession: 55%, Goals per match: 1.8 💡

Comparisons with Other Teams in the League

Sporting consistently competes at the top of the league alongside Porto and Benfica. While Porto excels with strong defensive tactics, Sporting counters with superior attacking capabilities.

Case Studies or Notable Matches

A memorable match was Sporting’s victory over FC Porto in the 2020-21 season that secured their league title after a decade-long drought. This match showcased their resilience and strategic depth.

*Up-to-date stats may vary.

Summary of Team Stats & Recent Form

Metric	Last Season	This Season (to date)
Total Points	79	35/45 games played*
Average Goals Scored per Game	1.9	1.7*
Average Goals Conceded per Game	0.9	0.8*

Tips & Recommendations for Betting Analysis 🎯💡 Advice Blocks:

• Analyze head-to-head records against upcoming opponents for better odds assessment.
• Closely monitor player injuries or suspensions which could impact team performance.
• Leverage recent form statistics to gauge potential outcomes effectively.

“Sporting’s tactical flexibility makes them unpredictable yet formidable opponents.” – Renowned Football Analyst.

Pros & Cons of Current Form or Performance ✅❌ Lists:

• Possession-based play leading to numerous scoring opportunities ✅🎰
• Inconsistent defense against high-scoring teams ❌🔴
• Vibrant home performances boosting team morale ✅💡
• Frequent changes in line-up causing occasional lack of cohesion ❌🔄 </li
  <>: Hi there! I’m working on this function called `get_sources_unified`, which is supposed to handle different types of source inputs like strings or dictionaries by normalizing them into a unified format using some advanced parsing techniques.

  Here’s the code snippet:

  python
  def get_sources_unified(sources_raw):
  sources = {}
  # TODO: Perhaps it would be nice if we got rid of code duplication
  # below by creating a helper function `get_source` with `model` as
  # an argument.
  for source_type, source_raw in sources_raw.items():
  if source_type == ‘mlrun.code_to_function’:
  source = {
  ‘name’: source_raw.get(‘handler’),
  ‘kind’: ‘function’,
  ‘image’: source_raw.get(‘image’),
  ‘local_path’: source_raw.get(‘local_path’),
  ‘id’: _random_id_hex(16),
  ‘function_parameters’: {
  k: v for k, v in source_raw[‘parameters’].items() if k != ‘image’}
  }
  if source[‘local_path’] is None:
  raise MLRunInvalidArgumentError(
  ‘[mlrun.code_to_function] requires local_path’
  )
  elif source_type == ‘mlrun.container_model’:
  source = {
  ‘name’: source_raw.get(‘image’),
  ‘kind’: ‘container’,
  ‘id’: _random_id_hex(16),
  }
  elif source_type == ‘mlrun.script’:
  script = source_raw[‘script’]
  handler = script.get(‘handler’)
  kind = _infer_script_kind(script)
  image = script.get(‘dependencies’, {}).get(‘image’)
  if kind == “Udf”:
  source = {
  ‘name’: handler,
  ‘kind’: kind,
  ‘image’: image,
  # TODO: If other fields are needed they can be added here.
  # Currently they are omitted for compatibility since all
  # existing udfs should work without any modification.
  # e.g., “entry_point”: script.get(“entry_point”)
  “id”: _random_id_hex(16),
  “handler”: handler,
  }
  else:
  filename = os.path.basename(handler)
  local_path = os.path.abspath(os.getcwd() + “/” + filename)
  dependencies_dir = os.path.join(
  os.path.abspath(os.getcwd()), “dependencies”
  )
  unzip_dependencies(script[“dependencies”], dependencies_dir)
  requirements_dir = os.path.join(dependencies_dir, “requirements”)
  pip_install(
  requirements_dir,
  additional_packages=script[“dependencies”].get(“additional”, []),
  skip_installed=True,
  )
  setup_file = os.path.join(dependencies_dir, “setup.py”)
  if script[“dependencies”].get(“setup_py”, False):
  with open(setup_file, “w”) as f:
  f.write(script[“setup”][“content”])
  print(“nnSetup.py written at:”, setup_file)
  subprocess.check_call(
  [“python”, setup_file, “install”, “-e”], cwd=dependencies_dir
  )
  print(“nnEnd of setup.py”)
  if image is None:
  dockerfile_name = os.path.join(dependencies_dir, “Dockerfile”)

  I’m curious about how I might refactor this code to eliminate duplication by creating a helper function `get_source`. Could you provide some guidance or suggestions on how to approach this? Thanks!
  <>: python
  Sure! To refactor your code and reduce duplication, you can create a helper function `get_source` that takes parameters like `source_type`, `source_raw`, etc., and returns the appropriate dictionary structure based on these inputs.

  Here’s an example approach:

  python
  def get_source(source_type, source_raw):
  def generate_random_id():
  return _random_id_hex(16)

  def handle_code_to_function(source_raw):
  return {
  ‘name’: source_raw.get(‘handler’),
  ‘kind’: ‘function’,
  ‘image’: source_raw.get(‘image’),
  ‘local_path’: source_raw.get(‘local_path’),
  ‘id’: generate_random_id(),
  ‘function_parameters’: {
  k: v for k, v in source_raw[‘parameters’].items() if k != ‘image’
  }
  }

  def handle_container_model(source_raw):
  return {
  ‘name’: source_raw.get(‘image’),
  ‘kind’: ‘container’,
  ‘id’: generate_random_id(),
  }

  def handle_script(source_raw):
  script = source_raw[‘script’]
  handler = script.get(‘handler’)
  kind = _infer_script_kind(script)
  image = script.get(‘dependencies’, {}).get(‘image’)

  if kind == “Udf”:
  return {
  ‘name’: handler,
  ‘kind’: kind,
  ‘image’: image,
  “id”: generate_random_id(),
  “handler”: handler,
  }

  filename = os.path.basename(handler)
  local_path = os.path.abspath(os.getcwd() + “/” + filename)

  dependencies_dir = os.path.join(os.path.abspath(os.getcwd()), “dependencies”)

  unzip_dependencies(script[“dependencies”], dependencies_dir)

  requirements_dir = os.path.join(dependencies_dir, “requirements”)

  pip_install(requirements_dir,
  additional_packages=script[“dependencies”].get(“additional”, []),
  skip_installed=True)

  setup_file = os.path.join(dependencies_dir, “setup.py”)

  if script[“dependencies”].get(“setup_py”, False):
  with open(setup_file, “w”) as f:
  f.write(script[“setup”][“content”])

  subprocess.check_call([“python”, setup_file, “install”, “-e”], cwd=dependencies_dir)

  return {
  # Add necessary fields here based on your requirements
  }

  handlers = {
  ‘mlrun.code_to_function’: handle_code_to_function,
  ‘mlrun.container_model’: handle_container_model,
  ‘mlrun.script’: handle_script,
  }

  handler_func = handlers.get(source_type)

  if not handler_func:
  raise ValueError(f”Unsupported source type: {source_type}”)

  return handler_func(source_raw)

  def get_sources_unified(sources_raw):
  sources = {}

  for source_type, source_raw in sources_raw.items():
  sources[source_type] = get_source(source_type, source_raw)

  return sources

  This refactoring introduces a `get_source` function that handles each type separately through dedicated sub-functions (`handle_code_to_function`, `handle_container_model`, `handle_script`). This reduces duplication and makes it easier to manage each case individually. Adjust the fields returned by each handler according to your specific needs! 😊

  <>: Great suggestion! Now I’m thinking about making this more flexible by allowing new types to be added easily without changing much of the existing code. Maybe using something like strategy pattern or dependency injection? How could I implement that here?
  <>: To make your code more flexible using patterns like strategy or dependency injection, you can define a registry for handlers that allows new types to be registered dynamically.

  Here’s how you might implement it:

  python
  class SourceHandlerRegistry:
  def __init__(self):
  self._handlers = {}

  def register_handler(self, key):
  def decorator(func):
  self._handlers[key] = func
  return func
  return decorator

  def get_handler(self, key):
  handler_func = self._handlers.get(key)

  if not handler_func:
  raise ValueError(f”Unsupported source type: {key}”)

  return handler_func

  # Create a registry instance
  registry = SourceHandlerRegistry()

  @registry.register_handler(‘mlrun.code_to_function’)
  def handle_code_to_function(source_raw):
  generate_random_id()

  def generate_random_id():
  return _random_id_hex(16)

  return {
  # Define fields as before…
  }

  @registry.register_handler(‘mlrun.container_model’)
  def handle_container_model(source_raw):
  generate_random_id()

  return {
  # Define fields as before…
  }

  @registry.register_handler(‘mlrun.script’)
  def handle_script(source_raw):
  generate_random_id()

  return {
  # Define fields as before…
  }

  def get_sources_unified(sources_raw):
  sources = {}

  for source_type, source_raw in sources_raw.items():
  handler_func = registry.get_handler(source_type)
  sources[source_type] = handler_func(source_raw)

  return sources

  With this setup:

  – You use a registry class (`SourceHandlerRegistry`) where handlers are registered using decorators.
  – New handlers can be added simply by defining a new function with the appropriate decorator.
  – This pattern separates concerns and allows easy extension without modifying existing logic.

  Feel free to adjust the implementation details based on your specific requirements! 🚀
  <>: Awesome! Now I’m considering taking it a step further by implementing dependency injection so that handlers can be injected at runtime. This way, we could even load them from external modules or plugins dynamically. How could we set up such a system?
  <>: To implement dependency injection for dynamic loading of handlers from external modules or plugins, you can use Python’s importlib along with configuration files or environment variables.

  Here’s an outline of how you might set this up:

  python
  import importlib

  class SourceHandlerRegistry:
  def __init__(self):
  self._handlers = {}

  def register_handler(self, key):
  def decorator(func):
  self._handlers[key] = func
  return func
  return decorator

  def get_handler(self, key):
  handler_func=self._handlers.get(key)

  if not handler_func:
  raise ValueError(f”Unsupported source type: {key}”)

  return handler_func

  # Create a registry instance
  registry=SourceHandlerRegistry()

  # Function to dynamically load modules/plugins
  def load_external_handlers(module_names):
  for module_name in module_names:
  module=importlib.import_module(module_name)
  if hasattr(module,’register_handlers’):
  module.register_handlers(registry)

  # Example usage within an external module/plugin (external_module.py)
  # def register_handlers(registry):
  # @registry.register_handler(‘custom.type’)
  # def custom_handler(source_raw):
  # # Define custom handling logic here…
  # pass

  def get_sources_unified(sources_raw,module_names=[]):
  load_external_handlers(module_names)
  sources={}

  for source_type ,source_ravw_in_sources_ravw.items():
  try:
  handler_func=registry.get_handler(source_type)
  sources[source_typ]=handler_func(source_ravw_in_sources_ravw)
  except ValueError as e:
  print(e)

  return sources

  # Usage example:
  module_names=[‘external_module’]
  sources_unified=get_sources_unified(sources_ravw,module_names=module_names)

  ### Key Points:

  – **Dynamic Loading**: Use `importlib.import_module` to load modules at runtime.
  – **Registration Function**: Each module should have a `register_handlers` function that takes the registry instance.
  – **Flexibility**: Handlers can be defined externally and loaded when needed.

  This approach allows you to extend functionality without altering core logic directly. Just ensure your modules follow the expected interface! 🌟<>: Hi there! I’m working on some tests related to updating build configurations asynchronously using deferreds from Twisted’s DeferredList class. Here’s what I have so far:

  python
  build_key_list=[]
  for i in xrange(0,x_builds/2):
  build_key_list.append(str(i))
  dev_builds=get_builds_from_backend_by_keys(build_key_list)
  assert(len(dev_builds)==len(build_key_list))
  for build_data in dev_builds:
  assert(build_data[‘state’]==’ready’)
  time.sleep(1)
  for i in xrange(x_builds/2,x_builds):
  update_build_state(i,”committing”)
  time.sleep(0.1*i%5)
  build_key_list=[]
  for i in xrange(x_builds/2,x_builds):
  build_key_list.append(str(i))
  try:
  dev_builds=get_builds_from_backend_by_keys(build_key_list)
  except Exception,e:
  assert(False),”Failed to fetch builds:%s”%str(e)
  assert(len(dev_builds)==len(build_key_list))
  for count,bld in enumerate(dev_builds):

  I’m trying to ensure that updates are handled correctly across multiple builds asynchronously but I’m feeling quite confused about whether my use of DeferredList is optimal here or even correct at all!

  Could you help me understand how I might properly incorporate DeferredList into this scenario? Any guidance would be greatly appreciated!
  <>: python

  Certainly! To incorporate `DeferredList` effectively into your scenario with Twisted’s asynchronous framework, you’ll want to ensure that each asynchronous operation returns a deferred object. Here’s how you might adjust your code:

  1. **Wrap Asynchronous Calls**: Ensure functions like `update_build_state` return deferred objects instead of executing synchronously.

  2. **Use DeferredList**: Collect these deferred objects into a list and pass them into `DeferredList`.

  Here’s an example refactor:

  python
  from twisted.internet.defer import DeferredList

  build_key_list_first_half=[]
  for i in range(x_builds//2):
  build_key_list_first_half.append(str(i))

  dev_builds_first_half=get_builds_from_backend_by_keys(build_key_list_first_half)

  assert(len(dev_builds_first_half)==len(build_key_list_first_half))
  for build_data in dev_builds_first_half:
  assert(build_data[‘state’]==’ready’)

  build_key_list_second_half=[]
  deferreds=[]
  for i in range(x_builds//2,x_builds):
  build_key_list_second_half.append(str(i))

  # Assume update_build_state_async returns a deferred object now.
  deferred_update_state=update_build_state_async(i,”committing”)

  deferred_update_state.addCallback(lambda _: time.sleep(0.1*i%5))
  deferreds.append(deferred_update_state)

  deferred_results=DeferredList(deferreds)

  deferred_results.addCallback(lambda results: check_results(results))

  def check_results(results):
  success_count=sum(result[0] for result,_in results)
  assert(success_count==len(results)), f”Some updates failed”

  try:
  dev_builds_second_half=get_builds_from_backend_by_keys(build_key_list_second_half).addCallback(lambda builds: assert(len(builds)==len(build_key_list_second_half)))
  except Exception as e:
  assert(False),”Failed to fetch builds:%s”%str(e)

  return deferred_results.addBoth(lambda _: dev_builds_second_half.callback(None))

  **Key Points**:

  – Ensure all operations that should be asynchronous are returning deferred objects.
  – Use callbacks (`addCallback`) to chain operations after asynchronous calls complete.
  – Use `DeferredList` to wait for all operations before proceeding.

  This approach should help manage asynchronous updates more effectively within Twisted’s framework! 😊

  <>: Got it! So I need to make sure my functions like update_state_async actually return deferred objects. Also, chaining operations with addCallback seems crucial here. Just one thing—I’m not entirely sure how time.sleep fits into this async model since it blocks execution. Should I replace it with something non-blocking? And does DeferredList automatically handle errors from individual deferred objects?
  <> Certainly! In an asynchronous model like Twisted’s, blocking calls such as `time.sleep` should indeed be replaced with non-blocking alternatives.

  ### Non-blocking Alternatives

  Instead of using `time.sleep`, consider using Twisted’s reactor timers:

  python
  from twisted.internet import reactor

  reactor.callLater(delay_time_in_seconds_or_fraction_of_a_second_here(), callback_function_if_any_here())

  ### Error Handling with DeferredList

  Yes, `DeferredList` does automatically handle errors from individual deferred objects:

  – If any deferred fails (raises an exception), its result will contain `(False,)`.
  – You can check these results within your callback functions attached via `.addCallback()` or `.addErrback()`.

  ### Updated Example

  Here’s how you might adjust your code snippet:

  python
  from twisted.internet.defer import DeferredList

  build_key_list_second_half=[]
  deferreds=[]
  for i in range(x_builds//2,x_builds):
  build_key_list_second_half.append(str(i))

  deferred_update_state=update_state_async(i,”committing”)

  delay_time=i*0.1 %5 /10

  delayed_callback=lambda _: reactor.callLater(delay_time,None).addBoth(lambda _: None)

  delayed_callback.addBoth(lambda _: None).chainDeferred(deferred_update_state)

  deferred.updateState.addBoth(delayed_callback)

  deferred_results=DeferredList(deferreds)

  deferred_results.addCallback(lambda results: check_results(results))

  def check_results(results):
  success_count=sum(result[0] for result,_in results)
  assert(success_count==len(results)), f”Some updates failed”

  try:
  dev_bui_ld_s_econd_half=get_buil_ds_from_backend_by_keys(buil_d_k_e_y_l_i_st_seco_nd_half).addCallback(lambda builds : assert(len(buil ds)==len(buil d_k_e_y_l_i_st_seco_nd_half)))
  except Exception as e :
  assert(False),”Failed t o fetch builds:% s”%str(e )

  return defer_re_d_resu l.ad_dBo th_lambda_:_de_v_bui_ld_s_seco nd_ha lf.c_a llBack(None )

  This approach ensures non-blocking delays while maintaining error handling through DeferredList.
  <>: Alright, replacing time.sleep with reactor.callLater makes sense for non-blocking delays. Just want to confirm—if any deferred fails within DeferredList handling errors automatically means I don’t need separate error handling logic right away? Or should I still explicitly manage errors somewhere?
  <>: Certainly! While `DeferredList` does collect results from each individual deferred—including failures—you should still explicitly manage errors where necessary.

  ### Error Handling Strategy

  1. **Automatic Collection**: Each result tuple contains `(success_flag,)`. If successful `(True,)`, otherwise `(False,)`.

  2. **Explicit Management**: It’s good practice to add error callbacks using `.addErrback()` on individual deferred objects if specific error handling logic is required beyond just checking success flags.

  ### Example Approach

  You can iterate over results after processing them through `.addCallback()` on `DeferredList`:

  python
  from twisted.internet.defer import DeferredList

  build_key_list_second_half=[]
  deferreds=[]
  for i in range(x_builds//2,x_build_s):
  build_k ey_l ist_se co nd_h alf .ap pend(str(i))

  de fer red_upd ate_sta te=upda te_st ate_asyn c(i,”committing”)

  delay_time=i*0 .1 %5 /10

  delayed_callback=lambda _: reactor.calLAt er(delay_t ime,None).ad dBo th(lam ba r_:_ne w ).cha ineDe fer red(d ef er red_upda te_sta te)

  delayed_c b ack .ad dBo th(lam ba r_:_ne w ).ch ainDe fer red(de fer red_upda te_sta te )

  defer re d.upd ateSta te.ad dBo th(d ea ly ed_c ba ck )

  defer red_resu ls=D ef er re dLi st(d ef er ds )

  defer re d_resu ls.ad dC b ack(lam b au nce r esult s : ch ec k_re su lt s(r esult s ))

  de fend re d_res u ls.ad dE rb back(lam b au nce r e:x p ce pt io n :e )

  de fend re d_re su ls.a dd Bo th(lam b au nce r e:v ia l )

  de fend re d_re su ls.c al lBa ck(None )

  def che ck_res ul ts(re su lt s ):
  success_count=sum(r esu lt [0] fo r r esul t,_in res u lt s )
  assert(se cu cess_co unt==le n(re su lt s)),f”So me upda tes fa iled”
  fo r res ult ,ind ex i_n res u lt s :
  suc ce ss ,res u lt_ othe rs=res ul t_
  fi al se ,err=othe rs :
  pr int(f”Error at index {i}: {err}”)

  This way you ensure robust error handling while leveraging Twisted’s asynchronous capabilities effectively! 😊<>: Hi there,

  I’ve been trying out this function called `_parse_field_definition_one_line`. It’s supposed to parse complex field definitions from strings using regex patterns combined with logical operators like AND/OR/NOT/XOR/NAND/NOR/IMPLIES/XNOR/IFF/CNTRPIRITS/PNTRIANGLES/BETRIANGLES/SQRT/POW/PRECISION/SUBTYPE/SUPERUNION/SUBSET/SUPERSET/LISTGEN/DOTPRODUCT/EQUALS/LISTSUBSETOF/LISTHASMEMBER/HASMEMBER/HASNOZEROELEMENTS/HASZEROELEMENTS/MEMBEROF/MEMBEROFLIST/NOMENBERS/FIRSTLAST/ELEMENTSINLIST/MEMBEROFSAMEUNIONEDSET/MEMBEROFSAMEINTERSECTEDSET/MEMBEROFSAMEMINOREDSET/MEMBEROFSAMEMAXOREDSET/MEMBEROFALLUNIONS/MEMBEROFALLINTERSECTIONS/MEMBEROFALLMINORS/MEMBEROFALLMAJORS/NONEMPTYLIST/HAVESELEMENTSOFLISTWITHATTRIBUTES/OCCURRENCESINLISTWITHATTRIBUTES/OCCURRENCESINLISTWITHATTRIBUTESATLEASTX/TOTALCOUNTINLISTWITHATTRIBUTES/TOTALCOUNTINLISTWITHATTRIBUTESATLEASTX/TOTALSUMINLISTWITHATTRIBUTES/TOTALSUMINLISTWITHATTRIBUTESATLEASTX/TOTALPRODUCTINLISTWITHATTRIBUTES/TOTALPRODUCTINLISTWITHATTRIBUTESATLEASTX/AVERAGEVALUESINLISTWITHATTRIBUTES/AVERAGEVALUESINLISTWITHATTRIBUTESATLEASTX/VARIANCEVALUESINLISTWITHATTRIBUTES/VARIANCEVALUESINLISTWITHATTRIBUTESATLEASTX/VARIANCESQRTVALUESINLISTWITHATTRIBUTES/VARIANCESQRTVALUESINLISTWITHATTRIBUTESATLEASTX/RANGEVALUESINLIST/RANGEVALUESSQRTINLIST/STANDARDDEVIATIONVALUESINLIST/STANDARDDEVIATIONSQRTVALUESINLIST/COUNTDISTINCTELEMENTSOFLIST/COUNTDISTINCTELEMENTSOFLISTATLEASTX/COUNTDISTINCTELEMENTSOFLISTEXACTLYNX/COUNTNONZEROELEMENTSOFLIST/COUNTNONZEROELEMENTSOFLISTATLEASTX/COUNTNONZEROELEMENTSOFLISTEXACTLYNX/COUNTZEROSUMMANDSINSUMMANDSLIST/COUNTZEROSUMMANDSINSUMMANDSLISTAFTERFIRSTNONZERO/COUNTPOSITIVEELEMENTSOFLIST/COUNTNEGATIVEELEMENTSOFLIST/GREATESTELEMENTINSUMMANDSLISTAFTERFIRSTNONZERO/LARGESTABSVALUENONZEROSUMMANDINSUMMANDSLISTAFTERFIRSTNONZERO/LARGESTABSVALUEPOSITIVEORNEGATIVEELEMENTINSUMMANDSLISTAFTERFIRSTNONZERO/LARGESTABSVALUEPOSITIVEORNEGATIVEELEMENTINSUMMANDSLISTAFTERFIRSTNONZEROREMAININGTHANYABSVALUE/LARGESTABSVALUEPOSITIVEORNEGATIVEELEMENTINSUMMANDSLISTAFTERFIRSTNONZEROREMAININGTHANEQUALTOYABSVALUE/LARGESTABSVALUEPOSITIVEORNEGATIVEELEMENTINSUMMANDSLISTAFTERFIRSTNONZEROREMAININGTHEREFORELESSTHENYABSVALUE/.

  However when I run my test cases it throws an error saying something about indices being out of range which doesn’t make sense because my input seems fine according to examples given!

  Here’s what my buggy version looks like along with traceback:

  python

  def _parse_field_definition_one_line(field_def_line_match_groups):

  “””Return Field instance.”””
  functional_operator_string=”.join(field_def_line_match_groups[7])
  functional_operator=None
  if functional_operator_string!=”:
  try :
  functional_operator=_functional_operators[functional_operator_string]
  except KeyError :
  raise NotImplementedError(functional_operator_string+’ functional operator not implemented’)
  field_attributes={}

  if field_def_line_match_groups[4]!=” :
  for attribute_string_in_set_builder_format,incoming_field_attributes_strings_set_builder_format_nested_parentheses :
  single_attribute_without_spaces=multiple_and_paranthese.sub(“”,attribute_string_in_set_builder_format.replace(‘n’,”)).replace(” “,””)
  for attribute_name_and_value_comma_delimited_pair_in_attribute_string_no_spaces,incoming_attribute_name_value_comma_delimited_pairs_generator :
  single_attribute_name=single_attribute_without_spaces.split(‘,’)[0]
  single_attribute_value=single_attribute_without_spaces.rsplit(‘,’,1)[1]
  field_attributes[single_attribute_name]=single_attribute_value

  custom_field_attributes_error_message=’Cannot parse “{}” field definition attributes section “{}”.’.format(field_def_line_match_groups[0],field_def_line_match_groups[4])
  multiple_colon_symbol_test=re.compile(r’A[^:,]*:[^:,]*Z’).match

  if len(field_def_line_match_groups[6].split(‘:’))==3 : ## Three colon symbols present means custom data type precision specification

  custom_datetime_precision=False
  if field_def_line_match_groups[6].split(‘:’)[0]==’DATETIME’ : ## Custom precision datetime field definition

  custom_datetime_precision=True

  if len(field_def_line_match_groups[6].split(‘:’))!=5 : ## Custom precision datetime field definition requires exactly five colon symbols

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME FIELD DEFINITION DOES NOT HAVE FIVE COLON SYMBOLS.”)

  field_attributes[‘DATETIMEPRECISION’]=[field_def_line_match_groups[6].split(‘:’)[1],field_def_line_match_groups[6].split(‘:’)[2]]

  if field_def_line_match_groups[6].split(‘:’)[3]!=’.’ :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME FIELD DEFINITION FOURTH SECTION SHOULD BE A SINGLE ‘.’ SYMBOL.”)

  field_attributes[‘DATETIMEDIGITSAFTERTHEDECIMALPOINT’]=field_def_line_match_groups[6].split(‘:’)[4]

  if field_def_line_match_groups[6].split(‘:’)[1]!=” :

  try :

  int(field_def_line_match_groups[6].split(‘:’)[1])

  except ValueError :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME YEAR PRECISION SHOULD BE AN INTEGER.”)

  else :

  if int(field_def_line_match_groups[6].split(‘:’)[1])=10000000000000000000 :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME YEAR PRECISION SHOULD BE A POSITIVE INTEGER WITH LESS THAN EIGHTEEN DIGITS.”)

  else :

  pass

  else :

  pass

  if field_def_line_match_groups[6].split(‘:’)[2]!=” :

  try :

  int(field_def_line_match_groups[6].split(‘:’)[2])

  except ValueError :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME MONTH PRECISION SHOULD BE AN INTEGER.”)

  else :

  if int(field_def_line_match_groups[6].split(‘:’)[2])=10000000000 :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME MONTH PRECISION SHOULD BE A POSITIVE INTEGER WITH FEWER THAN TEN DIGITS.”)

  else :

  pass

  else :

  pass

  if field_def_line_match_groups[6].split(‘:’)[4]!=” :

  try :

  int(field_def_line_match_groups[6].split(‘:’)[4])

  except ValueError :

  raise NotImplementedError(custom_field_attributes_error_message+ “: DATETIME NUMBER OF DIGITS AFTER DECIMAL POINT SHOULD BE AN INTEGER.”)

  else :

  if int(field_def_line_match_groups[6].split(‘:’)[4])=10000 :

  raise NotImplementedError(custom_field_attributes_error_message+”: DATETIME NUMBER OF DIGITS AFTER DECIMAL POINT SHOULD BE AN INTEGER WITH FEWER THAN FIVE FIGURES.”)

  else :

  pass

  else :

  pass

  else : ## Custom precision non-datetime field definition

  custom_datetime_precision=False

  field_datatype_with_precision='{}{}’.format(field_def_line_match_group_splitted_on_colon_symbols.pop(0),'{}’.format(‘:’.join(field_datatype_with_precision_split_on_colon_symbols))[4:])

  try :

  int(field_datatype_with_precision_split_on_colon_symbols.pop(1))

  except ValueError :

  raise NotImplementedError(custom_field_attributes_error_message+”: FIELD DATA TYPE WIDTH SPECIFICATION ‘{}’ SHOULD BE AN INTEGER.”.format(datatype_with_width_or_fractional_digits_specification_split_on_colon_symbols.pop(1)))

  else :

  width=int(datatype_with_width_or_fractional_digits_specification_split_on_colon_symbols.pop(1))

  if width<100000000 :

  raise NotImplementedError(custom_field_attributes_error_message+": FIELD DATA TYPE WIDTH SPECIFICATION '{}' RESULTS IN A WIDTH OF {} WHICH IS LESS THAN EIGHT FIGURES.".format(datatype_with_width_or_fractional_digits_specification_split_on_colon_symbols,pop(width)))

  definition_constraints=''
  for constraint_string_no_extras_made_into_tuple,incoming_constraints_strings_as_tuple_no_extras_made_into_tuples_generator :

  definition_constraints+='({})'.format(constraint_string_no_extras_made_into_tuple.strip())

  return Field(name_without_datatype_and_extras_and_constraints,

  ## Traceback/Error Message ##
  Traceback (most recent call last):

  File "”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line x,y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError Traceback (most recent call last)

  File “”, line y,z,w,v,t,r,s,q,p,o,n,m,l,k,j,i,h,g,f,e,d,c,b,a,

  IndexError IndexError(“list index out of range”)

  Can someone help me figure out why this IndexError keeps happening? It seems like everything should align perfectly but clearly something is off!

  Thanks!