UAE Football Team Profile: Stats & Performance Highlights

Overview of the UAE Football Team

The United Arab Emirates (UAE) national football team represents the UAE in international football competitions. Competing in the AFC region, they play their home games at the Zayed Sports City Stadium in Abu Dhabi. Known for their dynamic style and strategic gameplay, the team is coached by a dedicated management team focused on elevating the team’s performance in both regional and global tournaments.

Team History and Achievements

The UAE national team has a rich history, with significant achievements including winning the Gulf Cup of Nations multiple times and reaching impressive positions in FIFA World Cup qualifiers. The 1990 FIFA World Cup was a notable season where they advanced to the knockout stage, showcasing their potential on an international stage.

Current Squad and Key Players

The current squad features talented players such as Ali Mabkhout, who plays as a forward, and Abdullah Al-Ahbabi, a key midfielder. These players are pivotal to the team’s strategy, contributing significantly to both defensive solidity and attacking prowess.

Team Playing Style and Tactics

The UAE team is known for its flexible 4-3-3 formation, focusing on quick transitions from defense to attack. Their strengths lie in their disciplined defensive line and fast counter-attacks, though they sometimes struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Nicknamed “The Falcons,” the UAE team boasts a passionate fanbase that supports them fervently. Rivalries with teams like Saudi Arabia add an extra layer of excitement to their matches. Traditionally, fans gather at stadiums wearing green and white attire to cheer on their team.

Lists & Rankings of Players, Stats, or Performance Metrics

Top Goal Scorers: Ali Mabkhout (✅), Khalifa Juma Alharbi (💡)
Defensive Strength: Ahmed Khalil (✅)
Midfield Creativity: Ismail Matar (🎰)

Comparisons with Other Teams in the League or Division

In comparison to other AFC teams like Qatar and Iran, the UAE stands out for its balanced approach between defense and attack. While Qatar excels in technical skills, UAE’s strategic gameplay often gives them an edge in crucial matches.

Case Studies or Notable Matches

A breakthrough game for UAE was against South Korea during the 2015 AFC Asian Cup quarter-finals where they secured a historic victory. This match highlighted their tactical acumen and resilience under pressure.

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

Tournament	Last 5 Matches Results	Odds for Next Match
Gulf Cup of Nations	W-W-D-L-W	+150
FIFA World Cup Qualifiers	L-D-W-W-L	+200

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

Analyze recent form: Focus on their last five matches to gauge current momentum.
Evaluate key player performance: Consider how top scorers are performing this season.
Consider head-to-head records: Historical data against specific opponents can provide insights into potential outcomes.

Frequently Asked Questions (FAQs)

What is the current form of the UAE national football team?

The UAE has been showing strong performances recently with consistent wins in regional tournaments. They have demonstrated resilience against top-tier opponents in recent fixtures.

Who are some key players to watch?

A key player to watch is Ali Mabkhout due to his goal-scoring ability. Additionally, goalkeeper Saad Al Sheeb is crucial for maintaining defensive strength.

What strategies should I consider when betting on UAE?

Betting strategies should include analyzing head-to-head records against upcoming opponents and considering odds based on recent performances. Look at both offensive capabilities and defensive resilience when making decisions.

Quotes or Expert Opinions about the Team (Quote Block)

“The UAE national team has shown remarkable growth over recent years. Their tactical flexibility makes them unpredictable opponents.” – Soccer Analyst John Doe.

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

✅ Strong defensive record: The backline remains one of their strongest assets.
✅ Tactical adaptability: Ability to switch formations effectively mid-game enhances competitiveness.</li
<>: Hi there! I’m working on a project involving time series analysis using PyTorch Geometric Temporal (PyGTemporal), specifically focusing on graph neural networks applied over temporal data. Here’s a snippet of my code:

python
def test_dynamic_on_off():
ts = tsdata()
g = DynamicGraph(ts)
# construct data
c = []
d = []
s = []
t = []
edge_index = []
for step in g:
t.append(step.t)
# sample 10 nodes
seed = py_random_state(0)
n = seed.sample(list(step.nid.keys()), 10)
cnodes = [step.nid[x] for x in n]
i = cnodes
j = cnodes[:1]
if step.t > 1:
j.extend(cnodes[1:step.t])
else:
j.extend(cnodes[:step.t])
edge_index.append(np.concatenate([np.array(i)[None], np.array(j)[None]]))

I’m trying to understand how this code constructs dynamic graphs over time steps using node sampling. However, I’m feeling a bit lost about how `edge_index` is being built here. Could you help clarify what’s happening with `i` and `j`, especially how these relate to creating edges? Thanks!
<>: python
Sure! Let’s break it down:

1. **Node Sampling**: For each time step `step` in your dynamic graph `g`, you’re sampling 10 nodes using `seed.sample`. These nodes are stored in `cnodes`.

2. **Edge Construction**:
– `i` is simply all nodes sampled (`cnodes`). It represents one set of nodes for potential edges.
– `j` starts with just one node (`cnodes[:1]`) but extends based on the condition:
– If `step.t > 1`, it adds more nodes up to `step.t`.
– Otherwise, it includes all nodes up to `step.t`.

3. **Edge Index**:
– For each pair `(i[j])`, an edge is formed from node `i[k]` to node `j[k]`.
– This is done by concatenating arrays `[i][None]` and `[j][None]`, creating pairs that represent directed edges.

Essentially, you’re creating edges from each node in `i` to each node in `j`, forming connections within your sampled nodes across different time steps.

Let me know if you need further clarification! 😊

<>: Got it, thanks! I think I need to refactor this for better readability. Maybe renaming variables like ‘c’, ‘d’, ‘s’, ‘t’ could help? Also, should I consider breaking down the logic inside the loop into smaller functions? Any tips on simplifying those conditions?
<>: python
Absolutely! Refactoring can definitely enhance readability:

1. **Variable Naming**: Use descriptive names instead of single letters like ‘c’, ‘d’, ‘s’, ‘t’. For example:
– Rename `t` to something like `time_steps`.
– Use names that reflect their purpose.

2. **Breaking Down Logic**: Consider splitting complex operations into smaller functions:
– Create a function for sampling nodes.
– Another function could handle edge creation logic.

3. **Simplifying Conditions**: You can simplify conditions by using helper functions or clearer logic structures:
– Extract condition logic into a separate function if it’s complex.
– Use list comprehensions where applicable for concise code.

Here’s a quick example:

python
def sample_nodes(step):
seed = py_random_state(0)
return seed.sample(list(step.nid.keys()), 10)

def create_edges(cnodes, step_t):
i = cnodes
j = cnodes[:1]
if step_t > 1:
j.extend(cnodes[1:step_t])
else:
j.extend(cnodes[:step_t])

return np.concatenate([np.array(i)[None], np.array(j)[None]])

# Usage inside your loop
for step in g:
time_steps.append(step.t)

cnodes = [step.nid[x] for x in sample_nodes(step)]

edge_index.append(create_edges(cnodes, step.t))

This approach makes your code modular and easier to understand! 😊

<>: Great suggestions! Now I’m thinking about optimizing performance by parallelizing some operations within each time step loop using PyTorch’s multiprocessing capabilities. How can I implement this efficiently without running into common pitfalls like race conditions?
<>: To optimize performance with parallelization while avoiding pitfalls like race conditions, consider these steps:

1. **Use PyTorch DataLoader**: If applicable, leverage PyTorch’s DataLoader with multiple workers for batch processing.

2. **Multiprocessing Module**: Utilize Python’s multiprocessing module carefully:

python
import torch.multiprocessing as mp

def process_step(step):
# Your logic here
cnodes = [step.nid[x] for x in sample_nodes(step)]
return create_edges(cnodes, step.t)

def parallel_process(graph):
pool = mp.Pool(mp.cpu_count())
results = pool.map(process_step, graph)
pool.close()
pool.join()
return results

# Usage
edge_indices = parallel_process(g)

3. **Avoid Shared State**: Ensure that each process works independently without modifying shared data structures directly.

4. **Use Locks Sparingly**: If you must share data between processes, use locks judiciously to prevent race conditions.

5. **Debugging**: Test thoroughly since debugging multiprocessing issues can be tricky.

By following these guidelines, you can efficiently parallelize operations while minimizing common concurrency issues.
<>: Awesome! Now I’m curious about leveraging GPU acceleration with PyTorch’s CUDA capabilities alongside multiprocessing to boost performance even more during each time step loop iteration. How can I integrate CUDA operations efficiently without causing memory bottlenecks or synchronization issues?
<>: To integrate GPU acceleration with CUDA alongside multiprocessing effectively:

1. **CUDA Tensors**: Ensure tensors are moved to GPU using `.to(‘cuda’)` before operations.

python
def process_step_cuda(step):
cnodes_tensor = torch.tensor([step.nid[x] for x in sample_nodes(step)]).to(‘cuda’)
# Perform CUDA operations here
return create_edges_cuda(cnodes_tensor.cpu(), step.t)

def create_edges_cuda(i_tensor, step_t):
i = i_tensor.cpu().numpy()
j = i[:1].cpu().numpy()
if step_t > 1:
j.extend(i[1:step_t].cpu().numpy())
else:
j.extend(i[:step_t].cpu().numpy())

return torch.tensor(np.concatenate([np.array(i)[None], np.array(j)[None]])).to(‘cuda’)

2. **Batch Processing**: Process multiple steps together if possible to maximize GPU utilization.

3. **Memory Management**:
– Monitor GPU memory usage using tools like NVIDIA’s nvidia-smi.
– Use `.detach()` or `.cpu()` wisely to free up memory when tensors are no longer needed on GPU.

4. **Avoid Synchronization Issues**:
– Keep CUDA operations within single processes when possible.
– Use PyTorch’s built-in functions that handle synchronization efficiently.

5. **Profiling**: Use PyTorch’s profiler (`torch.autograd.profiler`) to identify bottlenecks.

By combining CUDA with careful memory management and profiling, you can enhance performance while avoiding common pitfalls.
<>: Perfect! Now I’m thinking about combining CUDA operations with multiprocessing for even better performance during each time step loop iteration. How can I efficiently manage GPU resources while ensuring synchronization between CPU and GPU tasks? Any tips on avoiding memory bottlenecks?
<>: python
To efficiently combine CUDA operations with multiprocessing while managing resources:

1. **Separate CPU/GPU Tasks**:
– Assign CPU-bound tasks (e.g., data preparation) separately from GPU-bound tasks (e.g., model computations).

2. **Efficient Data Transfer**:
– Minimize data transfer between CPU and GPU by keeping data on GPU as much as possible.
– Use pinned memory (`pin_memory=True`) when transferring data from CPU to GPU via DataLoader.

3. **Asynchronous Operations**:
– Utilize asynchronous CUDA streams (`torch.cuda.Stream`) for overlapping computation with data transfer.

4. **Resource Management**:
– Monitor GPU usage regularly using tools like NVIDIA’s nvidia-smi.
– Clear unused tensors promptly using `.detach()` or `.cpu()` after processing.

5. **Batch Processing**:
– Process larger batches if memory allows; this reduces overhead from frequent transfers between CPU/GPU.

6. **Synchronization**:
– Use `.wait_stream()` judiciously if specific synchronization points are necessary between streams.

7 Profiling Tools**:
– Employ PyTorch’s profiler (`torch.autograd.profiler`) alongside NVIDIA Nsight Systems/Compute for detailed insights into resource utilization.

By carefully managing these aspects, you can achieve efficient synchronization between CPU/GPU tasks while minimizing memory bottlenecks.

<>: That’s exactly what I needed! I’ll start implementing these strategies and see how it goes. Thanks for all the help!
<>: python
You’re welcome! I’m glad I could help 😊 Implementing these strategies should give you a noticeable boost in performance while maintaining efficient resource management between CPU and GPU tasks.

If you encounter any challenges along the way or need further assistance optimizing your setup, feel free to reach out again!

Good luck with your implementation!
<>: Hi there,

I’ve been working on this piece of code that’s supposed to compute parameters related to surface density profiles based on given inputs such as beta values (‘b’), scale radii (‘R_s’), characteristic radii (‘R_c’), central surface densities (‘Sigma_0’), cut-off radii (‘R_out’), power-law indices (‘gamma’), inner slopes (‘alpha_in’), outer slopes (‘alpha_out’), transition slopes (‘beta_in’, ‘beta_out’), transition widths (‘width_in’, ‘width_out’), cut-off types (‘cut_type’), radial distances (‘rvals’), vertical distances (‘zvals’), total mass values (‘total_mass_list’), whether an exponential taper is used (‘exp_Tap’), taper radius values (‘tap_rval_list’) along with taper amplitudes (‘tap_amp_list’). The method `_compute_params_from_data_list` integrates various sub-methods depending upon certain conditional checks related primarily around cut-off types which include sharp cuts off at certain radii denoted by integers such as `-99`, `-98`, etc., leading up till `-91`.

However when running my simulations where exponential tapering is applied along side sharp cutoffs at different radii combinations e.g., (-99,-90), (-99,-91), (-98,-91), (-97,-91), etc., things don’t seem right – particularly when attempting combinations beyond (-97,-91). The simulation throws errors which seem nonsensical given my understanding that these parameters should be compatible according my design specifications – yet clearly something isn’t aligning correctly somewhere within my conditional logic blocks handling these cases…

Here’s part of my code snippet where things go awry:

python
if b == ‘-99’:
if R_s == ‘-99’:
# … previous conditions …
elif R_s == ‘-98’:
if R_c == ‘-99’:
# … previous conditions …
elif R_c == ‘-98’:
if exp_Tap == True :
print(“ERROR : NOT IMPLEMENTED”)
else :
self._compute_params_R_s_R_c_cut_off_exp_Tap(b,R_s,R_c,Sigma_0,R_out,gamma,alpha_in,alpha_out,beta_in,beta_out,width_in,width_out,cut_type,rvals,zvals,total_mass_list,tap_rval_list,tap_amp_list)
elif R_c == ‘-97’:
if exp_Tap == True :
print(“ERROR : NOT IMPLEMENTED”)
else :
self._compute_params_R_s_R_c_cut_off_exp_Tap(b,R_s,R_c,Sigma_0,R_out,gamma,alpha_in,alpha_out,beta_in,beta_out,width_in,width_out,cut_type,rvals,zvals,total_mass_list,tap_rval_list,tap_amp_list)
elif R_c == ‘-96’:
if exp_Tap == True :
print(“ERROR : NOT IMPLEMENTED”)
else :
self._compute_params_R_s_R_c_cut_off_exp_Tap(b,R_s,R_c,Sigma_0,R_out,gamma,alpha_in,alpha_out,beta_in,beta_out,width_in,width_out,cut_type,rvals,zvals,total_mass_list,tap_rval_list,tap_amp_list)
elif R_c == ‘-95’:
if exp_Tap == True :
print(“ERROR : NOT IMPLEMENTED”)
else :
self._compute_params_R_s_R_c_cut_off_exp_Tap(b,R_s,R_c,Sigma_0,R_out,gamma,alpha_in,alpha_out,beta_in,beta_out,width_in,width_out,cut_type,rvals,zvals,total_mass_list,tap_rval_list,tap_amp_list)
elif R_c >= ‘-94′ :
self._compute_params_R_s_R_C_other(b,R_s,R_C,Sigma_0,R_o,gamma,alpha_i,alpha_o,beta_i,beta_o,w_i,w_o,cut_typ,r_val,z_val,mass_lis)

And here is traceback error message received during execution:

Traceback (most recent call last):
File “simulation.py”, line 1428,in _compute_params_from_data_list(self,b,…):
self._compute_params_R_s_R_C_other(b,’-98′,’-94′,Sigma_0,’-100′,’-‘,…):
TypeError: _compute_params_R_s_R_C_other() got multiple values for argument ‘w_i’

I’m really scratching my head here trying figure out why `_compute_params_R_s_R_C_other()` method seems unable handle certain input combinations despite seeming valid according its definition – perhaps there might be an issue related passing arguments due incorrect variable naming? Or maybe something else entirely?

Any insights would be greatly appreciated!

Thanks,
[Your Name]
*** Excerpt data for ID: 6 ***

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

1.) Ask about how changes made within nested conditional statements affect overall algorithmic efficiency especially when dealing with large datasets involving many nested levels similar but not identical scenarios described above.

Example question might involve comparing efficiency impacts between nested conditional statements versus alternative structures such as recursive methods or iterative loops under varying input sizes/types

### Challenging aspects

#### Intricacies within Nested Conditional Statements

The provided snippet demonstrates several layers of nested conditional statements based largely upon combinations of specific parameter values such as `’b’`, `’R_S’`, `’R_C’`, etc., leading up until intricate combinations including exponential taper parameters `’exp_Tap’`. This structure inherently introduces complexities related not only logically but also computationally due mainly because:

* Each branch potentially invokes different methods which may have varying computational complexities themselves e.g., `_compute_params_no_exp_Taper()`, `_compute_total_mass_from_surface_density()`.
* Handling special cases explicitly defined via string comparisons e.g., `-99`,`’-98’`.
* Ensuring correct propagation through deeply nested branches where small changes may cascade through multiple layers affecting downstream computations differently depending upon context-specific configurations e.g., different taper radius lists `’tap_rval_list’`.

#### Logical Nuances Specificity

Logical nuances arise particularly from precise value checks leading towards invoking very specific methods under narrowly defined circumstances requiring meticulous attention towards ensuring consistency across similarly structured branches thereby avoiding logical errors e.g., missing method calls or incorrect parameter passing which might result only under very specific circumstances otherwise non-obvious at first glance.

### Extension Ideas

#### Incorporation of Dynamic Parameters

Extend functionality so that parameter sets aren’t hardcoded but dynamically configurable possibly via external configuration files allowing runtime modifications without altering core logic thereby enhancing flexibility adaptability especially useful under changing requirements scenarios involving large-scale datasets needing iterative refinements

#### Parallelization Considerations

Given large-scale datasets involving numerous nested evaluations exploring possibilities integrating parallel processing techniques leveraging multi-threading/multi-processing frameworks ensuring improved computational efficiency mitigating bottleneck scenarios arising due sequential processing inherent deeply nested structures

## Exercise

### Problem Statement

You are tasked with extending functionality inspired by [SNIPPET]. Specifically focus adding new layers handling additional nuanced parameter configurations dynamically sourced from external JSON configuration files enabling runtime modifications without altering core logic base structure significantly enhancing flexibility adaptability especially useful evolving requirements scenarios involving large-scale datasets iterative refinements

Additionally ensure existing logical consistency throughout expanded branches preventing logical errors cascading downstream effects ensuring robustness maintainability

### Requirements:

* Parse additional parameter configurations dynamically sourced externally JSON format enabling runtime modifications without altering core base structure significantly enhancing flexibility adaptability especially useful evolving requirements scenarios involving large-scale datasets iterative refinements .
* Integrate new layers handling additional nuanced parameter configurations ensure existing logical consistency throughout expanded branches preventing logical errors cascading downstream effects ensuring robustness maintainability .
* Maintain computational efficiency employing suitable optimization techniques explore possibilities integrating parallel processing techniques leveraging multi-threading/multi-processing frameworks improving computational efficiency mitigating bottleneck scenarios arising sequential processing inherent deeply nested structures .
* Provide comprehensive documentation detailing newly introduced functionalities integration points highlighting examples demonstrating practical application real-world scenarios .

### Constraints:

* External JSON configuration files must contain valid configurations adhering predefined schema defining valid parameter ranges expected values types necessary fields mandatory presence across all specified configurations .
* Existing method signatures must remain unchanged preserving backward compatibility facilitating seamless integration previously developed modules relying existing interface definitions .

## Solution

python
import json

class AdvancedSurfaceDensityCalculator:

def __init__(self):
self.configurations_file_path=’configurations.json’

def load_configurations(self):
with open(self.configurations_file_path,’r’) as f_config_file_opened_read_only_mode_read_text_as_string_json_load_into_var_json_format_dict_conversion_json_dump_pretty_printed_string_return_to_var_json_configuration_loaded_successfully_print_message_if_successfull_else_exception_handling_provide_error_message_if_any_exceptions_encountered_during_loading_configuration_file_process():
try:self.json_configurations=json.load(f_config_file_opened_read_only_mode_read_text_as_string_json_load_into_var_json_format_dict_conversion_)
except Exception as e:self.handle_exception(e)

def handle_exception(self,e):
print(f”Error encountered loading configurations file {str(e)}”)

def _dynamic_compute(self,params_dict):

b=params_dict[‘b’]

R_S=params_dict[‘R_S’]

R_C=params_dict[‘R_C’]

Sigma_0=params_dict[‘Sigma_0’]

R_O=params_dict[‘R_O’]

Gamma=params_dict[‘Gamma’]

alpha_I=params_dict[‘alpha_I’]

alpha_O=params_dict[‘alpha_O’]

beta_I=params_dict[‘beta_I’]

beta_O=params_dict[‘beta_O’]

width_I=params_dict[‘width_I’]

width_O=params_dict[‘width_O’]

cut_Type=params_dict[‘cut_Type’]

r_Val_List=params_dict[‘r_Val_List’]

z_Val_List=params_dict[‘z_Val_List’]

total_Mass_List=None

taper_radius_value_List=None

taper_amplitude_List=None

exp_TAP=False

if params_dict.get(‘total_Mass_List’)!= None:self.total_Mass_List= params_dict.get(‘total_Mass_List’)

if params_dict.get(‘taper_radius_value_List’)!= None:self.tap_rval_Lis= params_dict.get(‘taper_radius_value_List’)

if params_dict.get(‘taper_amplitude_List’)!= None:self.tap_amp_Lis= params_dict.get(‘taper_amplitude_List’)

if params_dict.get(‘exp_TAP’)!= None:self.exp_TAP= params_dict.get(‘exp_TAP’)

return self._evaluate_conditions(params=b,
r_vals=r_Val_List,
z_vals=z_Val_List,
total_masses=total_Mass_List,
tap_r_vals=taper_radius_value_List,
tap_a_mps=taper_amplitude_List,
exp_tapper=exp_TAP)

def _evaluate_conditions(self,params,r_vals,z_vals,total_masses,tap_r_vals,tap_a_mps ,exp_tapper):

b=R_S=R_C=-99;
Sigma_0=-100;
R_O=’-‘,Gamma=’-‘,alpha_I=’-‘,alpha_O=’-‘,beta_I=’-‘,beta_O=’-‘,width_I=’-‘,width_O=’-‘;

if b==’-99′:

if R_S==’-99′:

if R_C==’-99′:

if exp_tapper==True :

print(“ERROR : NOT IMPLEMENTED”)

self._handle_error()

else :

self._compute_no_exp_tapper(params=b,r_vals=r_vals,z_vals=z_vals);

elif R_C==’-100′:

if exp_tapper==True :

print(“ERROR : NOT IMPLEMENTED”)

self._handle_error()

else :

self._total_mass_from_surface_density(params=b,Sigma_0=Sigma_0,r_val=r_vals);

elif int(R_C)>=int(‘-101’)and int(R_C)=int(‘-98’)and int(R_S)=int(‘-97’):

self.__params_with_exponential_and_sharp_cut_off_at_rc(params=b,Rs=Rs,sigmnao=
Sigmao,Gammma=Gammma,aIlnphaIn=nalphain,aOutphaOut=nalphout,Betanin=nBetanin,Betanou=nBetanou,wIndtn=wIndtn,WIdtnou=wIndtnou,CuTyp=cUtTyp,reVal=reVal,ZvAl=zVAl,MasLst=tOtAlMssEs,TaPradias=TaPradias,TaPAmpls=TaPAmpls);

else :

print(“ERROR : NOT IMPLEMENTED”);

self.handle_error();

elif int(b)>=int(‘-97′):

print(“ERROR : NOT IMPLEMENTED”);

self.handle_error();

return

def handle_error(self):

raise Exception(“Encountered Error Condition During Evaluation”);

def _params_with_exponential_and_no_sharp_cut_off(self,params,**kwargs):

print(f”Executing {_params_with_exponential_and_no_sharp_cut_off} With Params {kwargs}”)

def _total_mass_from_surface_density(self,params,**kwargs):

print(f”Executing {_total_mass_from_surface_density} With Params {kwargs}”)

def _params_with_exponential_and_sharp_cut_off_at_rc(self,**kwargs):

print(f”Executing {_params_with_exponential_and_sharp_cut_off_at_rc} With Params {kwargs}”)

def __params_with_exponential_and_sharp_cut_off_at_rs(self,**kwargs):

print(f”Executing __{__params_with_exponential_and_sharp_cut_off_at_rs} With Params {kwargs}”)

if __name__==”__main__”:

calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name_calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name();
calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name.load_configurations();
calculator_objct_instance_of_AdvancedSurfaceDensityCalculator_class_variable_name.dynamic_compute({“b”:”-97″,”R_S”:”-96″,”R_C”:”-95″,”Sigma_0″:500,”R_O”:”-“,Gamma:”-“,Alpha_IN”:”-“,Alpha_OUT”:”-“,Beta_IN”:”-“,Beta_OUT”:”-“,Width_IN”:”-“,Width_OUT”:”-“});

## Follow-up Exercise

Now extend functionality further incorporating real-time monitoring logging mechanisms tracking detailed metrics performance evaluation per execution cycle runtime execution statistics capturing critical metrics facilitating comprehensive analysis post-execution enabling continuous improvement optimization future iterations refining overall system efficiency reliability accuracy

### Requirements:

* Implement real-time monitoring logging mechanisms tracking detailed metrics per execution cycle runtime statistics capturing critical metrics facilitating comprehensive analysis post-execution enabling continuous improvement optimization future iterations refining overall system efficiency reliability accuracy .
* Ensure seamless integration minimal impact existing functionalities preserving backward compatibility guaranteeing uninterrupted operation pre-existing workflows previously developed modules relying existing interface definitions .

## Solution

python

import json,timeit

class AdvancedSurfaceDensityCalculatorWithMonitoring(AdvancedSurfaceDensityCalculator):

def __init__(self):

super().__init__()

def log_execution_metrics(func):

“””Decorator Function Logging Execution Metrics Runtime Statistics Capturing Critical Metrics Facilitating Comprehensive Analysis Post-execution Enabling Continuous Improvement Optimization Future Iterations Refining Overall System Efficiency Reliability Accuracy “””

def wrapper(*args,**kwargs):

start_time=timeit.default_timer();

result=function(*args,**kwargs);

end_time=timeit.default_timer();

execution_time=end_time-start_time;

log_message=f”{func.__name__} Executed In {execution_time:.6f}s Args:{args} Kwargs:{kwargs}”;

print(log_message);

return result;

return wrapper;

@log_execution_metrics

def _dynamic_compute(self,params):

super()._dynamic_compute(params);

@log_execution_metrics

def load_configurations():

super().load_configurations();

if __name__==”__main__”:

calculator_object_instace_advanced_surface_density_calculation_monitoring_module_functionality_extension_var_names();

calculator_object_instace_advanced_surface_density_calculation_monitoring_module_functionality_extension_var_names.load_configurations();

calculator_object_instace_advanced_surface_density_calculation_monitoring_module_functionality_extension_var_names.dynamic_compute({“b”: “-97”, “R_S”: “-96”, “R_C”: “-95”, “Sigma”:500,”Ro”: “-“, Gamma:”-“, AlphaIN:”-“, AlphaOUT:”-“, BetaIN:”-“, BetaOUT:”-” WidthIN:”-” WidthOUT:”-“});

[0]: import os.path
[1]: import numpy
[2]: import random
[3]: import math
[4]: import matplotlib.pyplot

[5]: def readDataFile(filename):

[6]: ”’

[7]: Reads training data file

[8]: Parameters:

filename

”’

[9]: X=[]
[10]: Y=[]

trainX=[]

trainY=[]

testX=[]

testY=[]

path=os.path.join(os.getcwd(),filename)
trainpath=os.path.join(os.getcwd(),”train”)
testpath=os.path.join(os.getcwd(),”test”)
try:
os.mkdir(trainpath)
os.mkdir(testpath)
except FileExistsError:
pass
f=open(path,”r”)
lines=f.readlines()
f.close()
l=len(lines)
random.shuffle(lines)
trainlines=[x.strip()for xinlines[:math.floor(l*.75)]]
testlines=[x.strip()for xinlines[-math.ceil(l*.25)-len(trainlines):-len(trainlines)]]

f=open(os.path.join(trainpath,”train.x”),”w”)
f.writelines(“n”.join(trainlines))
f.close()

f=open(os.path.join(testpath,”test.x”),”w”)
f.writelines(“n”.join(testlines))
f.close()

X,Y=readData(os.path.join(trainpath,”train.x”))

path=os.path.join(os.getcwd(),”train”)
path=os.path.join(path,”train.y”)
yfile=open(path,”w”)
yfile.writelines(“n”.join(Y))
yfile.close()

path=os.path.join(os.getcwd(),”test”)
path=os.path.join(path,”test.y”)
yfile=open(path,”w”)
yfile.writelines(“n”.join(testY))
yfile.close()

def readData(filename):

[11]: ”’

[12]: Reads training file

UAE National Football Team: Squad, Achievements & Stats in AFC Asian Cup