CF Zamora: Premier Team Profile - Squad, Achievements & Stats

Overview of CF Zamora

CF Zamora is a professional football club based in Zamora, Spain. Competing in the Segunda División B, the team was founded in 1928. The club is currently managed by coach Juan Carlos García and plays its home matches at Estadio Ruta de la Plata.

Team History and Achievements

Throughout its history, CF Zamora has experienced several notable seasons. The team reached its peak performance during the 1980s when it secured multiple promotions to higher divisions. Although it has not won major national titles, CF Zamora has consistently been a competitive force within its league, often finishing in mid-table positions.

Current Squad and Key Players

The current squad features key players like Miguel Torres (Forward), Javier López (Midfielder), and Carlos Martínez (Goalkeeper). Miguel Torres is known for his scoring ability, while Javier López provides creativity and leadership on the field.

Team Playing Style and Tactics

CF Zamora typically employs a 4-3-3 formation, focusing on an attacking style of play with an emphasis on wing play. The team’s strengths lie in its fast-paced transitions and set-piece execution. However, they can be vulnerable defensively against teams with strong counter-attacking capabilities.

Interesting Facts and Unique Traits

The team is affectionately known as “Los Lobos” (The Wolves) by their fans. With a passionate fanbase, CF Zamora enjoys a fierce rivalry with nearby clubs. One unique tradition is the pre-match ritual where fans gather to sing traditional songs from the region.

Lists & Rankings of Players, Stats, or Performance Metrics

• Miguel Torres – Top Scorer: ✅
• Javier López – Assists Leader: 💡
• Carlos Martínez – Clean Sheets: 🎰

Comparisons with Other Teams in the League or Division

In comparison to other teams in Segunda División B, CF Zamora stands out for its strong offensive play but needs to improve defensively to compete more effectively at higher levels.

Case Studies or Notable Matches

A breakthrough game for CF Zamora was their 3-1 victory against Real Oviedo in 2019, which marked their return to form after a series of poor performances. This match showcased their tactical flexibility and resilience under pressure.

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

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

To make informed betting decisions on CF Zamora matches:

• Analyze recent form trends before placing bets.
• Closely watch player injuries and suspensions that may affect lineup strength.
• Evaluate head-to-head records against upcoming opponents for insights into potential outcomes.
• Bet on over/under goals if you notice patterns in their high-scoring games.

Famous Quotes About CF Zamora 🗣️ Quote Block 🗣️

“CF Zamora embodies resilience and passion—a true representation of football spirit.” – Former Coach Enrique Sánchez.

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

• The team’s aggressive attacking style often leads to high-scoring games (✅).
• Miguel Torres’ presence enhances offensive capabilities significantly (✅).
• The squad shows excellent teamwork during set pieces (✅).</li
  <>: Hi there! I’m working with this code snippet that implements a `PointNetSetAbstractionCN2Nor` class using PyTorch. It seems to be handling point cloud data by sampling points through FPS sampling and then grouping them using ball query methods. There’s also some transformation logic involved.

  Here’s the snippet:

  python
  class PointNetSetAbstractionCN2Nor(nn.Module):
  def __init__(self, stride=1, nsample=512, in_channel=6, mlp=[], get_v=True):
  super(PointNetSetAbstractionCN2Nor, self).__init__()
  self.stride = stride
  self.nsample = nsample
  self.mlp_convs = nn.ModuleList()
  self.mlp_bns = nn.ModuleList()
  self.get_v = get_v
  if self.get_v:
  self.v_mlps = nn.ModuleList()
  self.v_bns = nn.ModuleList()
  last_channel = in_channel
  for out_channel in mlp:
  self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))
  self.mlp_bns.append(nn.BatchNorm1d(out_channel))
  last_channel = out_channel
  if self.get_v:
  self.v_mlps.append(nn.Conv1d(last_channel+6, out_channel, 1))
  self.v_bns.append(nn.BatchNorm1d(out_channel))
  def forward(self,x):
  pytorch_total_params = sum(p.numel() for p in self.parameters())
  print(pytorch_total_params)
  sampled_points = pointnet_fp_module(x[:,:3], x[:,3:], nsample=self.nsample)
  grouped_points = sample_and_group(nsample=self.nsample,stride=self.stride,sales_mode=’D’,point_xyz=x[:,:3],feature=x[:,3:],kp_xyz=None,kp_features=None)
  collect_feature_list = []
  for i in range(len(self.mlp_convs)):
  collect_feature_list.append(torch.cat([grouped_points[:,:,:i].transpose(1, 2).contiguous(),grouped_points[:,:,i+6:].transpose(1,
  2).contiguous()],dim=1)) #BxNxSx((C*i+6*(S-i)))
  collect_feature_list[i] = F.relu(self.mlp_bns[i](self.mlp_convs[i](collect_feature_list[i]))).transpose(1,
  2).contiguous() #BxSxC
  if self.get_v:
  collect_feature_list[i] = torch.cat([collect_feature_list[i], grouped_points[:, :, :i+6].repeat(1,self.mlp_convs[i].out_channels//((i+6)), 1)], dim=2) #BxSxC’
  collect_feature_list[i] = F.relu(self.v_bns[i](self.v_mlps[i](collect_feature_list[i]))).transpose(1,
  2).contiguous() #BxC’S
  new_features=hcat(collect_feature_list,dim=’length’)
  return new_features,sampled_points

  I’m feeling a bit confused about how exactly the transformation logic works here with `mlp_convs` and `v_mlps`. Could you help clarify what role these transformations play within this class? Thanks!
  <>: python
  Certainly! In this `PointNetSetAbstractionCN2Nor` class, the transformations using `mlp_convs` and `v_mlps` are crucial for processing point cloud data.

  ### Role of `mlp_convs`:
  – **Purpose**: These layers perform feature transformations on grouped point features.
  – **Process**: For each layer defined in `mlp`, a convolutional layer (`nn.Conv1d`) followed by batch normalization (`nn.BatchNorm1d`) is applied.
  – **Functionality**: They transform input features into higher-level representations by applying learned weights.

  ### Role of `v_mlps`:
  – **Purpose**: These layers are used when `get_v` is `True`, adding additional transformations involving both input features and positional information.
  – **Process**: Similar to `mlp_convs`, but they concatenate additional positional information before applying transformations.
  – **Functionality**: They enhance feature representation by incorporating spatial context.

  ### Overall Transformation Logic:
  – **Grouping**: Points are sampled and grouped using FPS sampling and ball query methods.
  – **Feature Concatenation**: Features from different channels are concatenated before passing through MLP layers.
  – **Activation**: ReLU activation functions are applied after each transformation to introduce non-linearity.
  – **Output**: The transformed features are concatenated across different layers to form the final output.

  This process allows the model to learn complex patterns from point cloud data effectively. 😊

  <>: Got it! Now I’m thinking about optimizing this class by reducing redundant computations during feature concatenation. Also, I want to ensure that operations are efficiently vectorized for better performance on large datasets. Any suggestions on how I could achieve this?
  <>: To optimize your class for better performance:

  ### Reduce Redundant Computations:
  1. **Pre-compute Static Values**: If certain values or operations remain constant across iterations (like indices), compute them once outside loops.

  ### Efficient Vectorization:
  1. **Batch Operations**: Ensure operations are batched wherever possible instead of iterating over individual elements.

  – Use PyTorch’s built-in functions like `torch.bmm` for batch matrix multiplications instead of looping through batches manually.

  ### Memory Management:
  1. **In-place Operations**: Use in-place operations (e.g., `add_`, `mul_`) where possible to save memory.

  ### Parallel Processing:
  1. **DataLoader Optimization**: Use PyTorch’s DataLoader with multiple workers (`num_workers`) to load data efficiently.

  ### Profiling:
  1. **Use Profilers**: Utilize tools like PyTorch Profiler or NVIDIA Nsight Systems to identify bottlenecks.

  By implementing these strategies, you should see improvements in both speed and efficiency when processing large datasets.
  <>: Alright! I’m thinking about implementing these optimizations directly into the code. For instance, I could pre-compute any static values outside loops and use batch operations more effectively with PyTorch functions like `torch.bmm`. Also, leveraging in-place operations could help with memory management. Here’s what I’m considering:

  python
  # Example optimization: Pre-compute static indices outside loop

  static_indices = compute_static_indices(…) # Hypothetical function

  for i in range(len(self.mlp_convs)):
  # Use pre-computed indices
  features_i = grouped_points[:, :, static_indices]

  # Efficient batch operation example
  transformed_features = torch.bmm(features_i.transpose(1, 2), weight_matrix)

  # In-place operation example
  transformed_features.add_(bias).relu_()

  collect_feature_list.append(transformed_features.transpose(1, 2))

  Does this approach make sense? Any other specific areas where I could apply these optimizations?
  <>: Your approach makes sense! Here are some additional suggestions:

  ### Specific Areas for Optimization:

  #### Pre-computation:
  – Ensure any repetitive calculations inside loops are moved outside if they don’t depend on loop variables.

  #### Batch Operations:
  – Replace manual loops with vectorized operations using PyTorch functions like `torch.einsum` if applicable for complex tensor manipulations.

  #### In-place Operations:
  – Be cautious with gradients; ensure that in-place operations do not interfere with autograd tracking unless you’re certain they won’t affect backpropagation.

  #### Memory Management:
  – Consider using mixed precision training (`torch.cuda.amp`) if you’re working on GPUs to reduce memory usage without sacrificing much precision.

  #### Code Example Enhancement:

  python
  # Example optimization: Pre-compute static indices outside loop

  static_indices = compute_static_indices(…) # Hypothetical function

  for i in range(len(self.mlp_convs)):
  # Use pre-computed indices efficiently
  features_i = grouped_points[:, :, static_indices]

  # Efficient batch operation example using einsum if needed
  transformed_features = torch.einsum(‘bnc,cn->bn’, [features_i.transpose(1, 2), weight_matrix])

  # In-place operation example ensuring gradient safety
  transformed_features.add_(bias).relu_()

  collect_feature_list.append(transformed_features.transpose(1, 2))

  # Consider mixed precision if applicable
  with torch.cuda.amp.autocast():
  # Your forward pass code here

  These changes should help streamline your computations while maintaining functionality.
  <>: Great! I’ll start implementing these changes. I’ll focus on pre-computing static values outside loops and replacing manual loops with vectorized operations like `torch.einsum`. Also, I’ll ensure that any necessary gradients aren’t affected by in-place operations unless safe to do so. Here’s a refined snippet:

  python
  # Pre-compute static indices outside loop

  static_indices = compute_static_indices(…) # Hypothetical function

  for i in range(len(self.mlp_convs)):
  features_i = grouped_points[:, :, static_indices]

  # Use einsum for efficient batch operation
  transformed_features = torch.einsum(‘bnc,cn->bn’, [features_i.transpose(1, 2), weight_matrix])

  # Safe in-place operation ensuring gradient integrity

  transformed_features.add_(bias).relu_()
  collect_feature_list.append(transformed_features.transpose(1, 2))

  Do you think using mixed precision training would be beneficial here as well?
  <>: Using mixed precision training can be highly beneficial if you’re working with GPUs. It reduces memory usage significantly while maintaining accuracy through dynamic scaling of floating-point numbers during training.

  Here’s how you can integrate it:

  python
  from torch.cuda import amp

  scaler = amp.GradScaler()

  # Within your training loop

  with amp.autocast():
  output_new_features_sampled_points = model(input_data)

  # Scale loss before backward pass

  loss.backward()
  scaler.step(optimizer)
  scaler.update()

  This approach helps manage memory more efficiently without compromising model performance significantly. Just ensure your hardware supports it! 😊<>: Hey there,

  I’ve been trying to implement an asynchronous method called `_execute_sequence` which handles state transitions based on some conditions derived from received messages (`m`). It involves nested async calls which should manage state transitions smoothly between different states like “await_presentation”, “await_xpub_qr”, etc., based on whether certain conditions are met such as message type checks or specific payload contents.

  However something isn’t quite right—my code keeps throwing an error when trying to transition states under certain conditions involving QR codes or public keys exchanges (“pubkey_qr”). Here’s my buggy version of `_execute_sequence`.

  python
  async def _execute_sequence(
  self,
  node_id: NodeID,
  switched_state_id: Tuple[str],
  ) -> None:
  logger.info(
  “RADAR_AUX_UNIT”,
  node_id=node_id.name,
  switched_state_id=_switched_state_id_to_str(switched_state_id),
  executed=__name__,
  )
  aux_unit_state = await self._storage.async_load_aux_unit_state(node_id)
  assert aux_unit_state is not None

  if aux_unit_state.state != SwitchState.ON
  or aux_unit_state.switches == SwitchMap():
  logger.debug(
  “RADAR_AUX_UNIT execute sequence skipped”,
  node_id=node_id.name,
  switches=_switches_tuple_to_string(aux_unit_state.switches),
  state=aux_unit_state.state.name,
  executed=__name__,
  )
  return

  target_map_size_is_reached = len(aux_unit_state.switches) == self._count_of_switches_required_for_target_map
  + AUX_UNIT_STARTING_SWITCHES_COUNT

  target_map_reached_flag_idx_found_in_switches_ids_lst_len_minus_one_position
  = target_map_size_is_reached
  and switched_state_id[-1] == f”{AuxUnitSwitchId.TARGET.value}{AuxUnitSwitchValue.ON.value}”

  starting_switch_on_and_target_map_not_reached_or_target_map_reached_flag_not_set
  = aux_unit_state.switches.get(SwitchMapKey.START.value) == SwitchMapValue.ON.value
  and not target_map_size_is_reached
  or not target_map_reached_flag_idx_found_in_switches_ids_lst_len_minus_one_position

  starting_switch_on_and_target_map_reached_and_target_map_reached_flag_set
  = aux_unit_state.switches.get(SwitchMapKey.START.value) == SwitchMapValue.ON.value
  and target_map_size_is_reached
  and target_map_reached_flag_idx_found_in_switches_ids_lst_len_minus_one_position

  if starting_switch_on_and_target_map_not_reached_or_target_map_reached_flag_not_set:
  logger.info(
  “RADAR_AUX_UNIT SKIPPING SEQUENCE BECAUSE ”
  “START SWITCH IS ON AND TARGET MAP REACHED FLAG IS ”
  f”NOT SET YET WITH {switched_state_id=} AND {aux_unit_state=}”,
  node_name=node_id.name,
  executed=__name__,
  )

  elif starting_switch_on_and_target_map_reached_and_target_map_reached_flag_set
  or switched_state_id == AUX_UNIT_ENDING_SWICHS_STATES_TUPLE
  or switched_state_id == ()
  or aux_unit_must_be_stopped(node_id):

  logger.info(
  “RADAR_AUX_UNIT Executing Sequence”,
  switch_data=_switch_data_tuple_to_string(switched_state_id),
  node_name=node_id.name,
  executed=__name__,
  )

  await self._nodes_service.async_send_command(
  node=node_id,
  command=NodeCommandType.EXECUTE_SEQUENCE,
  params={“sequence_type”: “default”},
  )

  def _command_removed_from_queue_was_response_to_the_last_command_sent(
  self,
  last_command_sent_context: Optional[CommandContext],
  ) -> bool:

  last_command_message_processed_event_type
  : Optional[Union[EventType[AuxCommandReceivedQueuedEvent], EventType[AuxCommandReceivedEvent]]]

  try:

  last_command_message_processed_event_type
  , _
  , _
  , _
  , _, _last_command_message_processed_event_metadata,
  _
  , _last_command_message_processed_event_node_uuid_obj
  , _, _
  , _
  , _last_command_message_processed_event_timestamp
  , _, _, _, _
  /
  *_, *_,
  *_last_command_message_processed_event_payload_arr,
  *_,
  *_last_command_message_processed_event_extra_metadata_arr,
  *_  = last_command_sent_context

  assert isinstance(_last_command_message_processed_event_metadata[“message”], str)
  assert isinstance(_last_command_message_processed_event_node_uuid_obj.uuid,
  UUID)

  last_command_message_received_str__not_none_or_empty_space_only_str__is_true
  : bool

  last_command_message_received_str__not_none_or_empty_space_only_str__is_true = (

  (_last_command_message_processed_event_metadata[“message”] is not None)
  &
  (_last_command_message_processed_event_metadata[“message”].strip() != “”)

  )

  event_payload_arr_length_gt_zero__is_true = len(_last_command_message_processed_event_payload_arr) > 0

  event_payload_arr_contains_dict_with_key_msg_type_eq_aux_cmd_get_status_reply__is_true = (

  event_payload_arr_length_gt_zero__is_true &
  (_last_command_message_processed_event_payload_arr[0][“msg_type”] == AuxCommandGetStatusReply.msg_type)

  )

  event_timestamp_present_and_node_uuid_present_are_true = (

  (_last_command_message_processed_event_timestamp is not None)
  &
  (_last_command_message_processed_event_node_uuid_obj.uuid is not None)

  )

  event_matches_the_last_comamnd_msg_received__is_true = (

  last_command_message_received_str__not_none_or_empty_space_only_str__is_true &
  event_payload_arr_contains_dict_with_key_msg_type_eq_aux_cmd_get_status_reply__is_true &
  event_timestamp_present_and_node_uuid_present_are_true

  )

  except ValueError as e_val_err_excp_instance:#raised_by_assert_statements_if_any

  return False

  else:#no exception raised_by_assert_statements_if_any

  return event_matches_the_last_comamnd_msg_received__is_true

  finally:#executed_always_no_matter_except_block_raised_exception_or_not

  del last_command_sent_context,last_node,last_node_uuid,last_aux_info_from_storage,
  last_aux_info_from_storage_is_not_none,last_aux_info_from_storage_has_at_least_one_entry,
  last_aux_info_from_storage_entries,last_aux_info_from_storage_entries_length_gt_zero,last_entry_of_last_aux_info_from_storage,
  entry_is_instance_of_dict,last_entry_of_last_aux_info_from_storage_keys_contain_at_least_one_match_for_each_item_of_substrs_array,last_substrs_array,
  substr,last_substr_was_found_in_last_entry_of_last_aux_info_from_storage_keys_are_all_True,last_substrs_array_len_gt_zero,
  substrs_exist_in_last_entry_of_last_aux_info_from_storage_keys,last_substring_was_found_in_last_entry_of_last_aux_info_from_storage_keys,is_all_True,arr_substrings_exists_in_dict_keys,is_all_True,arr_substrings_exists_in_dict_keys_bool_value,arr_substrings_exists_in_dict_keys_bool_value_len_gt_zero,arr_substrings_exists_in_dict_keys_bool_value_first_element,arr_substrings_exists_in_dict_keys_bool_value_first_element_eq_True,arr_substrings_exists_in_dict_keys_bool_value_first_element_eq_False,event_matches_the_last_comamnd_msg_received,event_timestamp_present_and_node_uuid_present_are_true,event_payload_arr_contains_dict_with_key_msg_type_eq_aux_cmd_get_status_reply,event_payload_arr_length_gt_zero,last_command_message_received_str__not_none_or_empty_space_only_str__is_true,_event_nodes_uuid_obj,_event_nodes_uuid_obj_is_not_none,_event_nodes_uuid_obj_is_instance_of_UUID_class,_event_nodes_uuid_obj_as_UUID_class,_event_nodes_uuid_as_STR,_event_timestamp_is_not_None,_event_timestamp_as_datetime_object,_event_nodes_name_is_not_None,_event_nodes_name_as_STR,_event_extra_metadata_as_STR,_event_original_metadata_as_STR,_metadata_as_JSON_DICT_object,_metadata_JSON_DICT_object_keys_as_LIST_OF_STRs,__metadata_JSON_DICT_object_values_as_LIST_OF_ANY_TYPE,__metadata_JSON_DICT_objects_values_are_ALL_instances_of_Dict_class,__metadata_JSON_DICT_objects_values_are_ALL_have_at_least_ONE_match_among_substrs_array_elements,__metadata_JSON_DICT_objects_values_are_ALL_have_at_least_ONE_match_among_substrs_array_elements_AND_THESE_SUBSTRS_ARE_ALL_PRESENT_IN_THESE_VALUES_AS_KEYS,__substr,__substr_was_found_in_metadata_json_dict_objects_values_As_Dicts_And_These_SubStrs_Are_Present_As_Keys_In_These_Dicts_And_They_Have_Non_Emtpy_Non_Blank_String_Values_And_Have_At_Least_One_Common_Key_Between_Them_And_Last_Event_Nodes_Name_And_Last_Event_Nodes_UUID_AS_Str,__common_key_between_metadata_dicts_values_dicts_And_Last_Event_Nodes_Name_And_Last_Event_Nodes_UUID_AS_Str,__common_key_between_metadata_dicts_values_dicts_And_Last_Event_Nodes_Name_And_Last_Event_Nodes_UUID_AS_Str_Is_Not_NoneOr_Empty_Space_StringOnly,__common_key_between_metadata_dicts_values_dicts_And_Last_Event_Nodes_Name_And_Last_Event_Nodes_UUID_AS_Str_Is_Not_NoneOr_Empty_Space_StringOnly_Is_True,__metadata_dicts_values_dicts_have_non_empty_non_blank_string_values_for_common_key_between_them_and_last_event_nodes_name_and_last_event_nodes_UUID_AS_Str,__metadata_dicts_values_dicts_have_non_empty_non_blank_string_values_for_common_key_between_them_and_last_event_nodes_name_and_last_event_nodes_UUID_AS_Str_Is_True,is_all_True_AND,is_all_True_AND_BOOL_VALUExAND_BOOL_VALUExAND_BOOL_VALUE,arr_substrings_exists_in_dict_keys_bool_value_first_element_eq_False,and_cond_x_result,and_cond_y_result,and_cond_z_result,and_cond_x_y_result,and_cond_x_y_z_result,is_all_True_AND_BOOL_VALUExAND_BOOL_VALUExAND_BOOL_VALUE_LEN_GT_ZERO,is_all_True_AND_BOOL_VALUExAND_BOOL_VALUExAND_BOOL_VALUE_LEN_GT_ZERO_bool_val,x,y,z,x_y,x_y_z,is_all_True_AND_BOOL_VALUExAND_BOOL_VALUExAND_BOOL_VALUE_LEN_GT_ZERO_bool_val_len_gt_zero,x_y_z_len_gt_zero,is_all_True_AND_BOOL_VALUExAND_BOOL_VALUExAND_BOOL_VALUE_LEN_GT_ZERO_bool_val_first_element,and_cond_w_result,w,x_w,w_x,w_x_y,w_x_y_z,w_x_y_z_len_gt_zero,w_x_y_z_len_gt_zero_first_element,and_cond_w_x_result,and_cond_w_x_y_result,and_cond_w_x_y_z_result,event_matches_the_prevous_comamnd_msg_received

  def __should_stop_after_executing_current_sequence(self,node,node_unique_ID:str,messages):

  previusly_executed_sequence_succeeded_return_code,msg_was_successfully_executed_by_device,msg_was_failed_execution_by_device,msg_was_failure_to_execute_by_device,msg_was_timeout_during_execution,msg_was_some_other_error_during_execution,response_time_ms,time_delta_since_previusly_executed_sequence_succeeded_response_time_ms,maximum_time_delta_since_previusly_executed_sequence_succeeded_response_time_ms,maximum_time_delta_since_previusly_executed_sequence_succeeded_response_time_ms_default_value_for_when_it_doesnt_exist,NoneIfItDoesNotExistIfItExistsElseItsValue,messages_count,messages_count_max,messages_count_max_default_value_for_when_it_doesnt_exist,NoneIfItDoesNotExistIfItExistsElseItsValue,maximum_messages_count_before_stopping_after_executing_current_sequence,maximum_messages_count_before_stopping_after_executing_current_sequence_default_value_for_when_it_doesnt_exist,NoneIfItDoesNotExistIfItExistsElseItsValue,maximum_messages_count_before_stopping_after_executing_current_sequence_default_value,current_sequnce_should_be_stopped_due_to_timedelta_exceeding_maximum_timedelta_since_previusly_executed_sequence_succeeded_response_time_ms,current_sequnce_should_be_stopped_due_to_messages_count_exceeding_maximum_messages_count_before_stopping_after_executing_current_sequence,current_sequnce_should_be_stopped_due_to_timedelta_exceeding_maximum_timedelta_since_previusly_executed_sequence_succeeded_response_time_ms_OR_messages_count_exceeding_maximum_messages_count_before_stopping_after_executing_current_sequence,current_sequnce_should_be_stopped_due_to_timeout_during_execution,current_sequnce_should_be_stopped_due_to_some_other_error_during_execution,current_sequnce_should_be_stopped_due_to_failed_execution_by_device,current_sequnce_should_be_stopped_due_to_failed_execution_by_device_OR_timeout_during_execution_OR_some_other_error_during_execution,current_sequnce_should_be_stopped,did_previous_execute_sequencex_return_code_indicate_successfully_execute,msg_was_successfully_executed_by_device,msg_was_failed_execution_by_device,msg_was_failure_to_execute_by_device,msg_was_timeout_during_execution,msg_was_some_other_error_during_device,highest_priority_level,highest_priority_level_index,highest_priority_level_index_plus_one,highest_priority_level_index_plus_two,priority_levels,priority_levels_start_index,priority_levels_end_index,priority_levels_range,priority_levels_range_plus_two,priority_level,priority_level_start_index,priority_level_end_index,priority_level_range,next_highest_priority_level,next_highest_priority_level_start_index,next_highest_priority_level_end_index,next_highest_priority_level_range,next_highest_priority_level_range_plus_two,next_next_highest_priority_level,next_next_highest_priority_level_start_index,next_next_highest_priority_level_end_index,next_next_highest_priority_level_range,next_next_highest_priority_level_range_plus_two,further_higher_priorities_exist,further_higher_priorities_exist_counter,further_higher_priorities_exist_counter_max,further_higher_priorities_exist_counter_max_default_value_for_when_it_doesnt_exist,NoneIfItDoesNotExistIfItExistsElseItsValue,further_higher_priorities_exist_counter_max_default_value_for_when_it_doesnt_exist,NoneIfItDoesNotExistIfItExistsElseItsValue,further_higher_priorities_do_not_exist,did_previous_execute_sequencex_return_code_indicate_successfully_execute_OR_further_higher_priorities_do_not_exist,did_previous_execute_sequencex_return_code_indicate_successfully_execute_OR_further_higher_priorities_do_not_exist_IS_TRUE,did_previous_execute_sequencex_return_code_indicate_failed_execution_BY_DEVICE_OR_timeout_during_execution_OR_some_other_error_during_device,message,message_type,message_type_equals_AuxCommandGetStatusReply.message_type,messageTypeEqualsAuxCommandGetStatusReplyBoolVal,messageTypeEqualsAuxCommandGetStatusReplyBoolValIsTrue,messageTypeEqualsAuxCommandGetStatusReplyBoolValIsTrueAndDidPreviousExecuteSequencexReturnCodeIndicateSuccessfullyExecuteOrFurtherHigherPrioritiesDoNotExistIsTrue,AuxCommandGetStatusReplyMessageWasReceivedFromNodeInResponseToPreviousExecuteSequenceCall,AuxCommandGetStatusReplyMessageWasReceivedFromNodeInResponseToPreviousExecuteSequenceCallAndMessageTypeEqualsAuxCommandGetStatusReplyMsgType,AuxCommandGetStatusReplyMessageWasReceivedFromNodeInResponseToPreviousExecuteSequenceCallAndMessageTypeEqualsAuxCommandGetStatusReplyMsgTypeIsTrue,AuxCommandGetStatusReplyMessageWasReceivedFromNodeInResponseToPreviousExecuteSequenceCallAndMessageTypeEqualsAuxCommandGetStatusReplyMsgTypeIsTrueAndDidPreviousExecuteSequencexReturnCodeIndicateSuccessfullyExecuteOrFurtherHigherPrioritiesDoNotExistIsTrue,FalseIFAnyOfTheAboveConditionsAreFalseELSETrue,FalseIFAnyOfTheAboveConditionsAreFalseELSETrueIsFalse,FalseIFAnyOfTheAboveConditionsAreFalseELSETrueIsFalseBoolVal,FalseIFAnyOfTheAboveConditionsAreFalseELSETrueIsFalseBoolValLenGTZero,FalseIFAnyOfTheAboveConditionsAreFalseELSETrueIsFalseBoolValLenGTZeroFirstElement,FalseIFAnyOfTheAboveConditionsAreFalseELSETrueISFALSEBOOLVALLENGTZEROBOOLVALFIRSTELEMENTEQTRUE,WASRESPONSETOEXECUTESEQUENCECOMMANDWITHSTATUSREQUESTMSGTYPEORFURTHERHIGHERSERIOUSERRORLEVELPRIORITYLEVELRECEIVEDFROMNODEINRESPONSETOPREVIOUSEXECUTESEQUENCEREFERENCECONTEXT,TrueIFSoOtherwiseFALSE,TrueIFSoOtherwiseFALSEISTRUE,TrueIFSoOtherwiseFALSEISTRUEandDidpreviousexecutesequencexreturncodereferencesequencecontextindicatefailedexecutionbydevicetimeoutduringexecutionorsomeothererrorduringdevice,TrueIFSoOtherwiseFALSEISTRUEandDidpreviousexecutesequencexreturncodereferencesequencecontextindicatefailedexecutionbydevicetimeoutduringexecutionorsomeothererrorduringdeviceISTRUE,WASRESPONSETOEXECUTESEQUENCECOMMANDWITHSTATUSREQUESTMSGTYPEORFURTHERHIGHERSERIOUSERRORLEVELPRIORITYLEVELRECEIVEDFROMNODEINRESPONSETOPREVIOUSEXECUTESEQUENCEREFERENCECONTEXTAndDidpreviousexecutesequencexreturncodereferencesequencecontextindicatefailedexecutionbydevicetimeoutduringexecutionorsomeothererrorduringdevice,TrueIFSoOtherwiseFALSE,WASRESPONSETOEXECUTESEQUENCECOMMANDWITHSTATUSREQUESTMSGTYPEORFURTHERHIGHERSERIOUSERRORLEVELPRIORITYLEVELRECEIVEDFROMNODEINRESPONSETOPREVIOUSEXECUTESEQUENCEREFERENCECONTEXTAndDidpreviousexecutesequencexreturncodereferencesequencecontextindicatefailedexecutionbydevicetimeoutduringexecutionorsomeothererrorduringdeviceISTRUE,FalseIFNoneOfTheAboveConditionsAreMetOtherWiseTRUE,FalseIFNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContext,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVAL,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZERO,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENT,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENTEQTRUE,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENTEQTRUEandShouldStopAfterExecutingCurrentSequenceForNextHighestPriorityLevelReferenceContext,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENTEQTRUEandShouldStopAfterExecutingCurrentSequenceForNextHighestPriorityLevelReferenceContextboolval,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENTEQTRUEandShouldStopAfterExecutingCurrentSequenceForNextHighestPriorityLevelReferenceContextboolvallengtzero,FALSEifNoneOfTheAboveConditionsAreMetOtherWiseTRUEShouldStopAfterExecutingCurrentSequenceForGivenPriorityLevelReferenceContextBOOLVALLENGTZEROFIRSTELEMENTEQTRUEandShouldStopAfterExecutingCurrentSequenceForNextHighestPriorityLevelReferenceContextboolvallengtzerofirstelement,FALSEifNoneoftheaboveconditionsaremetotherwisesTrueshouldstopafterexecutingcurrentsequencesforgivenprioritylevelreferencecontextboolvallengtzerofirstelementeqtrueandshouldstopafterexecutingcurrentsequencesfornexthighestprioritylevelreferencecontextboolvallengtzerofirstelementeqtrue,

  previous_execute_sequency_reference_context:return_code=msg_was_successfully_executed_by_device,msg_was_failed_execution_by_device,msg_was_failure_to_execute_by_device,msg_was_timeout_during_execution,msg_was_some_other_error_during_device,

  response_time_ms=time_delta_since_previusly_executed_sequence_succeeded_response_time_ms,maximum_time_delta_since_previusly_executed_sequence_succeeded_response_time_ms=maximunmum_timedelta_seconds_before_considering_that_a_previous_call_to_an_Execute_Sequence_request_has_timmed_out,

  maximum_number_of_messages_before_considering_that_a_previous_call_to_an_Execute_Sequence_request_has_timmed_out=maximunmum_number_of_responses_expected_before_considering_that_a_previous_call_to_an_Execute_Sequence_request_has_timmed_out,

  current_sequnce_should_be_stopped_due_to_timedelta_exceeding_maximum_timedelta_since_previusly_executed_sequence_succeeded_response_time_ms=(time_delta_since_previusly_executed_sequence_succeeded_response_time_ms >= maximum_timedelta_seconds_before_considering_that_a_previous_call_to_an_Execute_Sequence_request_has_timmed_out),

  current_sequnce_should_be_stopped_due_to_messages_count_exceeding_maximum_messages_count_before_stoppping_after_excutecing_current_sequences=(messages_count >= maximunmum_number_of_responses_expected_before_considering_that_a_previous_call_to_an_Execute_Sequence_request_has_timmed_out),

  current_sequnce_should_be_stopped_due_totimeout_durning_exeuction=current_sequnce_should_be_stoppped_due_totimeout_durning_exeuction=(msg_was_timeout_during_excution),

  current_sequnce_should_be_stoppped_due_tosomthingelse_wrong=current_sequnce_shoudlbe_stoppped_due_tosomthingelse_wrong=(msg_was_some_other_error),

  current_sequnce_shoudlbe_stoppped_due_tofailed_exeuction=current_sequnece_shoudlbe_stoppped_due_tofailed_exeuction=(msg_was_failed_exeuction),

  current_seqsuece_shoulbdbe_stoppped=current_seqsuece_shoulbdbe_stoppped=(current_seqsuece_shoulbdbe_stoppped_due_totimeout_durning_exeuction|current_seqsuece_shoulbdbe_stopppeddue_tosomthingelsewrong|current_seqsuece_shoulbdbe_stopppeddue_tofailed_exeuction),

  did_prevoious_execute_sequecne_return_code_indicate_successfulyexeucte=(previous_execute_sequency_reference_context.return_code==msg_was_sucessfully_excuteued),

  further_higher_serious_error_lavel_priotity_levelsexist=FalseIFNoFurtherHigherSeriousErrorLavelPriotityLevelsExistOTHERWISETruethatiteratesuntiltherearenohigherlevelsoritreturnsfalse,

  Should_Stop_After_Excutng_Current_Sequene_For_Given_Priorty_Level_Reference_Context=False_IF_No_Futher_Higher_Series_Error_Lavel_Priortiy_Level_Reponse_Messagetypesreceivedfromnodewheniteratingthroughallserioserrorlevelpriortiesotherwisetrue,

  False_IF_No_Futher_Higher_Series_Error_Lavel_Priortiy_Level_Reponse_Messagetypesreceivedfromnodewheniteratingthroughallserioserrorlevelpriortiesotherwisetrue_IS_TRUE=False_IF_No_Futher_Higher_Series_Error_Lavel_Priortiy_Level_Reponse_Messagetypesreceivedfromnodewheniteratingthroughallserioserrorlevelpriortiesotherwisetrue_IS_TRUE,

  WAS_RESPONSE_TO_EXECUTE_SEQUENCE_COMMAND_WITH_STATUS_REQUEST_MSG_TYPE_OR_FURTHER_HIGHERSERIOUSERRORLEVELPRIORITY_LEVEL_RECEIVED_FROM_NODE_IN_RESPONSE_TO_PREVIOUS_EXECUTESQUENCECALL=True_IF_SO_OTHERWISE_FALSE,

  True_IF_SO_OTHERWISE_FALSE_IS_TRUE=True_IF_SO_OTHERWISE_FALSE_IS_TRUE,

  DID_PREVOIUS_EXECUTE_SEQUECNE_RETURN_CODE_INDICATE_SUCCESSFULLYEXCUTE=True_IF_SO_OTHERWISE_FALSE,

  DID_PREVOIUS_EXECUTE_SEQUECNE_RETURN_CODE_INDICATE_SUCCESSFULLYEXCUTE_IS_TRUE=DID_PREVOIUS_EXECUTE_SEQUECNE_RETURN_CODE