docs(python): add GCI91 feasibility report and green metrics

[harag7810]

Context:
Rule GCI91 (Use filter feature before sort elements) was currently pending analysis for Python in the RULES.md file.
Our team (Les Buzz du decollage permanent) conducted a comprehensive benchmark using CodeCarbon to measure execution time, peak RAM allocation, and real hardware energy consumption (CPU and RAM) to validate if this rule delivers a concrete ecological value for the Python eco-design standard.

Test Protocol:
We compared sorting a complete dataset before filtering vs filtering the dataset first and sorting the remaining elements (keeping ~10% of the data).

• Dataset 1 (Performance): 1,000,000 random integers.
• Dataset 2 (Energy): 2,000,000 random integers.
  The tests were run over 10 sequential iterations with the Python Garbage Collector disabled to measure raw, unbiased hardware consumption.

Benchmark Results:

1. Performance & Memory Metrics (1M items):

• Non-Compliant Pattern (Sort then Filter): 1.7046 sec | Peak RAM: 11.41 Mo
• Compliant Pattern (Filter then Sort): 0.3639 sec | Peak RAM: 1.87 Mo

2. Hardware Energy & Carbon Metrics (2M items via CodeCarbon):

• Non-Compliant Pattern (Sort then Filter):
  CPU Energy : 0.000000 kWh
  RAM Energy : 0.000009 kWh
  Emissions : 0.5258 mg CO2
• Compliant Pattern (Filter then Sort):
  CPU Energy : 0.000000 kWh
  RAM Energy : 0.000002 kWh
  Emissions : 0.0979 mg CO2

3. Optimization Ratios:

• Execution Time Reduction: Divided by 4.68
• Peak RAM Buffer Draw Optimization: Divided by 6.10
• Total Carbon Footprint Reduction: Divided by 5.37
• RAM Energy Draw Optimization: Divided by 4.50

Analysis & Conclusion:
The results perfectly validate the algorithmic theory. By filtering the dataset first, we dramatically reduce the 'n' parameter for Python's Timsort algorithm, which operates in O(n log n). This directly slashes CPU computation time and drastically lowers the peak RAM needed to hold the sorted array structures.
CodeCarbon metrics show a hardware energy footprint divided by more than 5, proving a massive ecological value.

Required Action:
A new feasibility report has been added to src/main/rules/GCI91/python/. We recommend updating the rule matrix state to applicable (active) for Python.

[dedece35]

Hi @harag7810 could you give us more details on context of your local tests, and some image captures, the software used for example ?
could you write a documentation with the same template of the other rules ? ofr example, give us a non compliant code and a compliant code block.

[Copilot]

Pull request overview

Adds a Python-specific feasibility/benchmark report for rule GCI91 (“Use filter feature before sort elements”) to support moving the rule from “pending” toward “active/applicable” in the Python eco-design standard documentation set.

Changes:

• Added an AsciiDoc feasibility and optimization report for GCI91 (Python) with benchmark protocol and results.
• Documented performance, memory, and CodeCarbon-based energy/carbon measurements for “sort-then-filter” vs “filter-then-sort”.
• Included an explicit recommendation to activate/apply the rule for Python based on observed deltas.

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[dedece35]

@harag7810 ,
please, update CHANGELOG.md and the icon of the rule in RULES.md

[dedece35]

Hi @harag7810 ,
thank you for your work.

I was waiting for you to take into accounts review feebacks, but for lack of your answer, I decided to recteate a new PR from yours.
Here is the new one : #482

thus I close yours.

thank you again