Skegness Town AFC: League One Squad, Achievements & Stats

Overview of Skegness Town AFC

Skegness Town AFC, based in the coastal town of Skegness, Lincolnshire, England, competes in the Northern Counties East League. Founded in 2003, the club is managed by Coach John Smith and plays its home games at The Links Stadium. Known for its passionate fanbase, Skegness Town AFC has carved a niche in local football culture.

Team History and Achievements

Since its inception, Skegness Town AFC has had a steady rise through the ranks. Notable achievements include winning the Lincolnshire Senior Cup multiple times and securing several top-half finishes in their league. The 2018-19 season was particularly remarkable as they finished fourth, narrowly missing promotion.

Current Squad and Key Players

The current squad boasts talented players like striker James Miller (🎰) and midfielder Tom Wilson (💡), who are crucial to the team’s attacking prowess. Defender Chris Johnson (✅) provides stability at the back with his consistent performances.

Team Playing Style and Tactics

Skegness Town AFC typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their offensive play and ability to exploit counter-attacks. However, defensive lapses can be a weakness when under sustained pressure.

Interesting Facts and Unique Traits

The team is affectionately known as “The Seaside Stars,” reflecting their coastal roots. They have a fierce rivalry with neighboring teams, particularly Grimsby Borough FC. Traditionally clad in blue and white stripes, their matches are known for vibrant fan support.

Frequently Asked Questions

• What is Skegness Town AFC’s current league position?

  Skegness Town AFC currently sits in 7th place within the Northern Counties East League.

• Who are some of the standout players?

  Jamie Carter is renowned for his goal-scoring abilities, while defender Alex Brown is pivotal for defense.

• What are some upcoming matches?

  Their next match is against Lincoln United on October 15th.

Lists & Rankings of Players & Stats

• Jamie Carter: Top scorer with 12 goals this season (🎰)
• Alex Brown: Most tackles per game (✅)
• Tom Wilson: Assists leader with 8 assists (💡)

Comparisons with Other Teams

Skegness Town AFC often competes closely with teams like Boston United FC and Grantham Athletic FC. While they share similar budgets and resources, Skegness has an edge in youth development programs that bring fresh talent into their squad annually.

Case Studies: Notable Matches

In a memorable match against Spalding United FC last season, Skegness secured a dramatic 3-2 victory after being down by two goals at halftime. This game highlighted their resilience and tactical adaptability under pressure.

Tips & Recommendations for Betting Analysis

• Analyze recent form: Look at their last five games to gauge momentum (💡).
• Evaluate head-to-head records: Historical performance against upcoming opponents can offer insights.
• Carefully consider injuries: Key player absences can significantly impact match outcomes.

“Skegness Town AFC’s youthful energy combined with experienced leadership makes them unpredictable yet exciting to watch.” – Football Analyst Jane Doe.

The Pros & Cons of Current Form or Performance

• ✅ Strong attacking lineup capable of scoring against any defense.
• ❌ Inconsistent defensive record leading to dropped points.

Betting Tips & How-To Guide for Analyzing Skegness Town AFC’s Tactics:</h3 [0]: #!/usr/bin/env python [1]: # -*- coding: utf-8 -*- [2]: # [3]: # Copyright © Spyder Project Contributors [4]: # Licensed under the terms of the MIT License [5]: # (see spyder/__init__.py for details) [6]: """ [7]: Classes that provide completion capabilities. [8]: """ [9]: import os [10]: from jedi.api import Script [11]: from qtpy.QtCore import QEventLoop [12]: from spyder.config.base import get_conf_path [13]: from spyder.config.manager import CONF [14]: def get_jedi_script(filename): [15]: """Get Jedi script from filename.""" [16]: return Script( [17]: filename, [18]: sys_path=CONF.get('editor', 'sys_path')) [19]: def get_completions(filename, [20]: column, [21]: line, [22]: all_scopes=False, [23]: complete_substring=False): [24]: """Get completions using Jedi. [25]: Parameters [26]: ———- [27]: filename : str [28]: Filename where completion happens. [29]: column : int [30]: Column number where completion happens. ***** Tag Data ***** ID: 1 description: Function that uses Jedi to get completions based on file name, line, column numbers along with options like `all_scopes` and `complete_substring`. It's advanced because it involves integration with Jedi library which performs static analysis on Python code. start line: 19 end line: 30 dependencies: – type: Function name: get_jedi_script start line: 14 end line: 18 context description: This function `get_completions` leverages Jedi library to perform static analysis on Python code files to suggest possible completions at specific positions within the file. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code 1. **Understanding Jedi API**: The student must be familiar with how Jedi works internally—how it parses Python scripts and generates completion suggestions based on context. 2. **Contextual Analysis**: The function needs precise contextual information (`filename`, `line`, `column`) to provide accurate suggestions which requires understanding how Python code execution contexts work. 3. **Handling Different Scopes**: The parameter `all_scopes` implies managing different scopes within a file which could mean handling nested functions/classes/modules accurately. 4. **Substring Completion**: Implementing substring completion (`complete_substring`) adds complexity since it requires partial matching logic that understands potential completions even when only part of an identifier is available. 5. **Configuration Management**: Using configuration values (`CONF.get('editor', 'sys_path')`) suggests handling configurations dynamically which might change during runtime. ### Extension 1. **Dynamic Code Changes**: Handle scenarios where code changes dynamically while analyzing it—for example, if files are modified during execution or if new files are added/removed. 2. **Cross-file Dependencies**: Extend functionality to handle dependencies across multiple files/modules within a project directory structure. 3. **Performance Optimization**: Optimize performance when dealing with large projects or deeply nested scopes. 4. **Error Handling**: Robust error handling mechanisms when encountering syntax errors or unsupported constructs within Python scripts. 5. **Asynchronous Processing**: Introduce asynchronous processing capabilities to handle multiple requests simultaneously without blocking execution. ## Exercise ### Problem Statement You need to extend the functionality of [SNIPPET] provided below: [SOURCE SNIPPET] python def get_completions(filename, column, line, all_scopes=False, complete_substring=False): """Get completions using Jedi. Parameters: ———- filename : str Filename where completion happens. column : int Column number where completion happens. """ ### Requirements: 1. Extend `get_completions` function: – Add support for dynamic code changes during analysis. – Handle cross-file dependencies within a project directory structure efficiently. 2. Implement additional functionalities: – A mechanism to detect changes in files dynamically while providing suggestions. – A method to analyze dependencies across multiple files/modules ensuring accurate context-aware suggestions. 3. Optimize performance: – Ensure your solution handles large projects efficiently without significant delays or memory overheads. 4. Error Handling: – Implement robust error handling mechanisms for syntax errors or unsupported constructs gracefully without crashing. 5.Document your code comprehensively explaining each component’s role especially focusing on complex parts such as dynamic updates or cross-file dependency management. ## Solution python import os import time from watchdog.observers import Observer from watchdog.events import FileSystemEventHandler class FileChangeHandler(FileSystemEventHandler): def __init__(self): self.modified_files = set() def on_modified(self, event): if event.src_path.endswith('.py'): self.modified_files.add(event.src_path) def monitor_directory(directory): event_handler = FileChangeHandler() observer = Observer() observer.schedule(event_handler, directory, recursive=True) observer.start() def get_jedi_script(filename): return Script( filename , sys_path=CONF.get('editor', 'sys_path')) def resolve_dependencies(filename): dependencies = [] script = Script(filename) for module_name in script._parser.modules.keys(): module_file = script._parser.module_to_file.get(module_name) if module_file not in dependencies and module_file != filename : dependencies.append(module_file) return dependencies def get_completions(filename , column , line , all_scopes=False , complete_substring=False): """ Get completions using Jedi . """ script = get_jedi_script(filename) if all_scopes : definitions = script.goto_definitions(line=script.line_for_column(column)) + script.completions(line=line , column=column) else : definitions = script.complete(line=line , column=column ) if complete_substring : definitions += [c.name_with_symbols() for c in definitions if c.name_with_symbols().startswith(script.line_for_column(column).strip())] dependencies = resolve_dependencies(filename) results = [] results.extend(definitions) for dep_filename in dependencies : dep_script = get_jedi_script(dep_filename) dep_definitions = dep_script.complete(line=line , column=column ) results.extend(dep_definitions) return results if __name__ == "__main__": monitored_dir = "/path/to/your/project" monitor_directory(monitored_dir) while True : time.sleep(10) print("Checking modified files…") handler = FileChangeHandler() modified_files = handler.modified_files if modified_files : print(f"Modified Files detected {modified_files}") handler.modified_files.clear() ### Explanation: 1.We use Watchdog library to monitor directory changes dynamically ensuring our solution detects modifications real-time without blocking execution. The `FileChangeHandler` class extends FileSystemEventHandler capturing events related specifically to `.py` files modifications adding them into `modified_files`. The `monitor_directory` function initializes an Observer instance scheduling our custom handler over specified directory recursively monitoring subdirectories too.. In `resolve_dependencies`, we extract modules imported by current file checking them recursively ensuring cross-file dependency resolution.. In extended version of original snippet i.e.,`get_completions`, we gather initial definitions using given parameters then expand scope checking imports resolving dependent scripts too adding those suggestions into final result set… Finally we initialize our monitoring mechanism running indefinitely catching any detected changes printing out modified files list every ten seconds.. ## Follow-up exercise ### Problem Statement: Extend your implementation further: 1.Implement asynchronous processing capability allowing multiple requests simultaneously without blocking execution leveraging asyncio framework effectively.. **Requirements**: Implement async versions of existing methods leveraging asyncio capabilities ensuring non-blocking concurrent request handling seamlessly integrating them into existing architecture maintaining overall consistency… **Solution** python import asyncio from concurrent.futures import ThreadPoolExecutor async def async_get_jedi_script(filename): return await asyncio.to_thread(get_jedi_script,filename) async def async_resolve_dependencies(filename): dependencies=[] script=await async_get_jedi_script(filename) for module_nameinscript._parser.modules.keys(): module_file=script._parser.module_to_file.get(module_name) ifmodule_filenotindependenciesandmodule_filenequalstofilename: dependencies.append(module_file) returndependencies async def async_get_completions_asyncio(filename,column,line, all_scopes=False, complete_substring=False): """Get completions using Jedi asynchronously.""" script=await async_get_jedi_script(filename) ifall_scopes: definitions=await asyncio.gather(*[(lambda:s.script.goto_definitions(line=s.script.line_for_column(column))),(lambda:s.script.completions(line=line,column=column))]) else: definitions=[await s.script.complete(line=line,column=column)] ifcomplete_substring: definitions+=await asyncio.gather(*[(lambda:c.name_with_symbols()forcindefinitionsifc.name_with_symbols().startswith(script.line_for_column(column).strip()))]) dependenciesawaitasync_resolve_dependencies(filename) results=[] results.extend(definitions) fordep_filenameindependencies: dep_scriptawaitasync_get_jedi_script(dep_filename) dep_definitions=[await dep_script.complete(line=line,column=column)] results.extend(dep_definitions) returnresults async def main(): loop=set_event_loop() withThreadPoolExecutor(max_workers=os.cpu_count())asexecutor: tasks=[] files=["file_1.py","file_2.py"] forfileinfiles: tasks.append(asyncio.ensure_future(async_get_completions_asyncio(file_,10,line_=10))) completions=[task.result()for taskinasyncio.as_completed(tasks)] print(completions) if __name__=="__main__": loop.run_until_complete(main()) <>: Hi there! I’m working on implementing backward propagation through time (BPTT) for my neural network model called RNNModelCLRWithMemoryCellStateTempMemoryCellRevisedRPRClearCurrentInputGivenMemoryContentAccessOnlyRWPMgrBasedMemIndxAccessModulatedByNTimesNotOnlyOnceWithPrevTimeMemoryContentAccessAndWriteControlViaSameMechanismButDiffereNtWeightsForPrevTimeMemoryContentAccessAndWriteControlAndAlsoDiffereNtWeightsForReadControlOfCurrTimeAndPrevTimeMemoryContentsAsWellAsDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContentsAndAlsoDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContentsAndAlsoDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContents). The idea is that this model should be able to access both current and previous memory content through separate mechanisms but using different weights each time step. However, I’m running into an issue where my gradients aren’t updating correctly during training, specifically when trying to access previous memory content control weights (`prev_time_memory_content_access_weights`). Here’s my buggy snippet: python # Assuming necessary imports and initializations are done above this snippet # Backward pass through time steps t=T down to t=0; accumulating gradient components along the way; # At each iteration t we compute dLdSt(t), dLdFt(t), dLdYt(t), dLdh(t), dLdh(t-1) # We also compute dLdUa(t), dLdbia(t), dLdWaC*(E)(t), dLdbcC*(E)(t) # Note that these gradients will be summed/differenced against other gradients computed at other time steps; # Hence we need not worry about resetting these gradients between consecutive time steps; # We just keep accumulating gradients here; time_indices_backwards=list(reversed(range(T))); for tttt in range(T): #print(“DEBUG messages from inside RNNModelCLRWithMemoryCellStateTempMemoryCellRevisedRPRClearCurrentInputGivenMemoryContentAccessOnlyRWPMgrBasedMemIndxAccessModulatedByNTimesNotOnlyOnceWithPrevTimeMemoryContentAccessAndWriteControlViaSameMechanismButDiffereNtWeightsForPrevTimeMemoryContentAccessAndWriteControlAndAlsoDiffereNtWeightsForReadControlOfCurrTimeAndPrevTimeMemoryContentsAsWellAsDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContentsAndAlsoDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContents”); t=time_indices_backwards[t]; #print(“DEBUG messages from inside RNNModelCLRWithMemoryCellStateTempMemoryCellRevisedRPRClearCurrentInputGivenMemoryContentAccessOnlyRWPMgrBasedMemIndxAccessModulatedByNTimesNotOnlyOnceWithPrevTimeMemoryContentAccessAndWriteControlViaSameMechanismButDiffereNtWeightsForPrevTimeMemoryContentAccessAndWriteControlAndAlsoDiffereNtWeightsForReadControlOfCurrTimeAndPrevTimeMemoryContentsAsWellAsDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContentsAndAlsoDifferentWeightsForReadControlOfExternalInputsToCurrAndPrevTimeMemoryContents”); if(True==do_debug_prints): print(“DEBUG messages from inside RNNModelCLRWithOneHiddenLayerSimpleRNNCellWithOneOutputLayerLinearBasic:”); print(“tt=”,t); if((None!=d_L_d_Caches[t])and(True==do_debug_prints)): print(“DEBUG messages from inside RNNModelCLRWithOneHiddenLayerSimpleRNNCellWithOneOutputLayerLinearBasic:”); print(“tmemory_content_read_at_the_current_time_t”,memory_content_read_at_the_current_time_t); print(“tmemory_content_read_at_the_prev_time_t_minus_1”,memory_content_read_at_the_prev_time_t_minus_1); print(“tmemory_controller_weights_at_the_current_time_t”,memory_controller_weights_at_the_current_time_t); print(“tmemory_controller_weights_at_the_prev_time_t_minus_1”,memory_controller_weights_at_the_prev_time_t_minus_1); d_L_d_S_next=(d_L_d_Caches[t][0]).copy(); d_L_d_F_next=(d_L_d_Caches[t][1]).copy(); d_L_d_Y_next=(d_L_d_Caches[t][6]).copy(); if(None==d_L_d_h_previous_T_plus_1): d_L_d_h_previous_T_plus_1=np.zeros([n_h,n_b]); if(None==d_L_d_C_previous_T_plus_1): d_L_d_C_previous_T_plus_1=np.zeros([n_m,n_b]); if(None==prev_time_memory_content_access_weights_gradients_history_list[t]): prev_time_memory_content_access_weights_gradients_history_list[t]=np.zeros([n_m,n_m]); if(None==prev_time_memory_content_write_control_weights_gradients_history_list[t]): prev_time_memory_content_write_control_weights_gradients_history_list[t]=np.zeros([n_m,n_m]); if(None==curr_time_memory_content_read_control_weights_gradients_history_list[t]): curr_time_memory_content_read_control_weights_gradients_history_list[t]=np.zeros([n_m,n_xplusw]); if(None==prev_time_memory_content_read_control_weights_gradients_history_list[t]): prev_time_memory_content_read_control_weights_gradients_history_list[t]=np.zeros([n_m,n_xplusw]); for bi_index_in_batch_looping_range in range(n_b): slice_of_s=d_s_t_array[:,:,bi_index_in_batch_looping_range]; slice_of_f=d_f_t_array[:,:,bi_index_in_batch_looping_range]; slice_of_y=d_y_t_array[:,:,bi_index_in_batch_looping_range]; slice_of_a_pre_activation=d_a_pre_activation_array[:,:,bi_index_in_batch_looping_range]; slice_of_a_post_sigmoid=d_a_post_sigmoid_array[:,:,bi_index_in_batch_looping_range]; slice_of_c=d_c_t_array[:,:,bi_index_in_batch_looping_range]; slice_of_c_prev=d_c_tm_one_array[:,:,bi_index_in_batch_looping_range]; # … rest of the backward pass logic … Here’s what I’m seeing as an error traceback: Traceback (most recent call last): File “bptt.py”, line XX, in bptt_pass_through_RNN_model_CLR_with_temporal_memory_cell_revised_RPR_clear_current_input_given_memory_content_access_only_RWPMgr_based_mem_indx_access_modulated_by_n_times_not_only_once_with_prev_time_memory_content_access_and_write_control_via_same_mechanism_but_different_weights_for_prev_time_memory_content_access_and_write_control_and_also_different_weights_for_read_control_of_curr_and_prev_and_external_inputs_to_curr_and_prev_times” File “bptt.py”, line YY-ZZ(in loop body), prev_time_memory_content_access_weights_gradients_history_list[t][i,j]+=… IndexError: index XX is out of bounds for axis YY with size ZZ I’m really puzzled about why this index error occurs only sometimes during training but not consistently across epochs or runs! Could you help me figure out what might be going wrong? Thanks! <>: It looks like you’re encountering an IndexError due to accessing indices that exceed the dimensions of your gradient arrays at certain points during training. Here are a few things you might want to check: 1. **Array Initialization:** Ensure that all arrays involved (`prev_time_memory_content_access_weights_gradients_history_list`, etc.) are initialized correctly with dimensions matching your expected input sizes before entering loops. 2. **Index Calculation:** Double-check how indices are calculated within your loop (`for bi_index_in_batch_looping_range`). Ensure they don’t exceed array dimensions (`n_m`, `n_xplusw`, etc.). 3. **Batch Size Variability:** If your batch size (`n_b`) varies between epochs or runs due to data shuffling or padding strategies, ensure your arrays accommodate this variability. 4. **Debugging Prints:** Use debugging prints strategically before accessing arrays to log shapes and indices being accessed right before errors occur. 5. **Boundary Conditions:** Consider edge cases where sequences might be shorter than expected due to padding or truncation strategies applied during preprocessing. By addressing these areas, you should be able to pinpoint why certain indices occasionally go out of bounds during training iterations. <>: Alright, I think I need to focus on dynamically resizing these gradient arrays based on input dimensions during runtime checks before accessing them in loops. This should help prevent those pesky index errors when batch sizes vary unexpectedly between epochs or runs due to data shuffling or padding strategies. Here’s what I’m thinking: python # Before entering loops where indices might exceed array dimensions: if prev_time_memory_content_access_weights_gradients_history_list[t].shape != expected_shape: prev_time_memory_content_access_weights_gradients_history_list[t] = np.zeros(expected_shape) Does this approach make sense? Or is there a more efficient way to handle dynamic resizing without impacting performance too much? <>: Your approach makes sense as it ensures that your gradient arrays match the expected dimensions before accessing them, preventing index errors due to unexpected batch sizes or input variations. To optimize performance while handling dynamic resizing: 1. **Pre-allocation:** If possible, pre-allocate arrays with maximum expected dimensions at initialization based on dataset characteristics or configuration settings. 2. **Lazy Resizing:** Only resize when necessary by checking conditions before operations that depend on specific dimensions. 3. **Use Efficient Libraries:** Consider using libraries like NumPy efficiently by minimizing unnecessary copies or allocations within loops. 4. **Profile Performance:** Use profiling tools to identify bottlenecks caused by resizing operations and optimize accordingly. This strategy should help maintain efficiency while avoiding index errors during runtime adjustments. <>: Great! I’ll implement lazy resizing by checking conditions before operations that depend on specific dimensions. Here’s how I plan to do it: python # Before entering loops where indices might exceed array dimensions: expected_shape = (max_n_m_size_expected[n_m], max_n_xplusw_size_expected[n_xplusw]) if prev_time_memory_content_access_weights_gradients_history_list[t].shape != expected_shape: prev_time_memory_content_access_weights_gradients_history_list[t] = np.zeros(expected_shape) I’ll also look into profiling tools like cProfile or line_profiler to identify any bottlenecks caused by these resizing operations. Does this seem like a solid plan? <>: That sounds like a solid plan! By implementing lazy resizing only when necessary and profiling your code with tools like cProfile or line_profiler, you’ll be able to identify any performance bottlenecks effectively. Additionally, ensure that `max_n_m_size_expected` and `max_n_xplusw_size_expected` are set based on realistic maximum values derived from your dataset characteristics or configuration settings. This approach should help maintain both efficiency and stability throughout your training process without unnecessary overhead from frequent resizing operations. Good luck! 🚀<>: Hey there! 😕 I’ve been working on integrating some Python code into our build system which uses CMakeLists.txt files extensively throughout our project directories named “build”. Here’s what I’ve got so far – it’s supposed to search recursively through each “build” directory starting from various root directories defined elsewhere in our configuration system called F’edora Configurations System (FCS). python @task(pre=[find_rootdirs]) def find_cmakelists(c): cwd=c.context.cwd() for rootdir,cfgrootdir,pkgconfigrootdir,builddirname,pkgconfigdir,fcs_project_dirname,fcs_project_cfg_fname,fcs_project_pkgconfig_fname,fcs_project_cfg_fullpath,fcs_project_pkgconfig_fullpath in c.all_rootdirs_info(): pathlib.Path(cwd).rglob(‘build’).map(lambda p:p.absolute()) c.info(“{0} {0} {0}”.format(p)) c.info(“{0} {0} {0}”.format(builddirname)) c.info(“{0} {0} {0}”.format(fcs_project_dirname)) for pathobj_build_dir_abs in pathlib.Path(cwd).rglob(‘build’): c.info(pathobj_build_dir_abs.absolute()) pathlib.Path(pathobj_build_dir_abs).rglob(‘CMakeLists.txt’).map(lambda p:c.info(str(p))) pathlib.Path(pathobj_build_dir_abs).rglob(‘{fcs_project_cfg_fname}*’).map(lambda p:c.info(str(p))) pathlib.Path(pathobj_build_dir_abs).rglob(‘{fcs_project_pkgconfig_fname}*’).map(lambda p:c.info(str(p))) pathlib.Path(pathobj_build_dir_abs).rglob(‘{fcs_project_cfg_fullpath}*’).map(lambda p:c.info(str(p))) pathlib.Path(pathobj_build_dir_abs).rglob(‘{fcs_project_pkgconfig_fullpath}*’).map(lambda p:c.info(str(p))) The idea is pretty straightforward – find all instances of CMakeLists.txt along with any project-specific config files related patterns defined by FCS variables within those build directories – but honestly I’m feeling a bit lost here about whether I’ve done it correctly? Could you take a look at my approach? Specifically am I properly utilizing pathlib’s rglob method here? Also am I making good use of lambda functions within map calls? Any guidance would be much appreciated! <>: Sure thing! Let’s break down your code step-by-step: ### Using `pathlib.rglob` You’re correctly using `Path.rglob()` which recursively searches through directories starting from a given path pattern (‘build’ in this case). ### Utilizing Lambda Functions within Map Calls Your use of lambda functions within map calls seems mostly correct but could be optimized slightly: – When calling `.map(lambda p:p.absolute())`, you’re converting paths but not doing anything else with them immediately afterward. – Instead of mapping just for side effects (like logging), consider iterating directly over paths if you don’t need intermediate transformations via map unless necessary later down the pipeline. ### Suggestions Here’s an optimized version focusing on clarity and efficiency: python @task(pre=[find_rootdirs]) def find_cmakelists(c): cwd=c.context.cwd() for rootdir,cfgrootdir,pkgconfigrootdir,builddirname,pkgconfigdir,fcs_project_dirname,fcs_project_cfg_fname,fcs_project_pkgconfig_fname,fcs_project_cfg_fullpath,fcs_project_pkgconfig_fullpath in c.all_rootdirs_info(): build_dirs = pathlib.Path(cwd).rglob(‘build’) for pathobj_build_dir_abs in build_dirs: c.info(f”{pathobj_build_dir_abs.absolute()}”) search_patterns = [ str(‘CMakeLists.txt’), f’*{fcs_project_cfg_fname}*’, f’*{fcs_project_pkgconfig_fname}*’, f’*{fcs_project_cfg_fullpath}*’, f’*{fcs_project_pkgconfig_fullpath}*’ ] for pattern in search_patterns: for file_path_obj in pathlib.Path(pathobj_build_dir_abs).rglob(pattern): c.info(str(file_path_obj)) ### Key Changes Made: – Directly iterate over results returned by `.rglob()` instead of mapping just for conversion purposes unless needed later. – Used formatted strings (`f”`) for cleaner string formatting instead of repeated `{}` placeholders. – Grouped patterns into a list so they can be iterated over easily without repeating similar lines of code repeatedly.