Refactor and Optimize Backend Calculations

[kurtgodel1]

Objective

To improve the performance and maintainability of the backend calculations in the Django REST framework. Refactoring will focus on optimizing the use of scientific libraries and ensuring efficient data handling.

Task List

• [x] Analyze existing backend calculations for performance bottlenecks.
• [x] Refactor code to optimize the use of scientific libraries like NumPy and SciPy.
• [x] Implement caching mechanisms where applicable to reduce computational load.
• [x] Ensure that all calculations are accurate and adhere to scientific standards.
• [x] Write unit tests to validate the correctness and performance of the optimized calculations.
• [x] Update API documentation to reflect any changes in the backend processing.

This issue is created as part of the ongoing effort to enhance the functionality and efficiency of the web application.

[kurtgodel1]

Progress Update on Issue 13: Refactor and Optimize Backend Calculations

We have completed the first task in Issue 13, which focused on analyzing existing backend calculations for performance bottlenecks. Here's a summary of the actions taken and findings:

• Logging and Profiling Implemented: Integrated django-silk for profiling and added logging in key backend views to monitor performance and gather detailed data.
• Initial Analysis Conducted: Conducted an initial review of the backend API views, focusing on calculation-intensive endpoints. Added profiling to functions like calculate_power_function.
• Data Collection: Started collecting data on performance metrics and logs. This data will be instrumental in identifying areas for optimization in the backend calculations.

We are now moving on to the next task of refactoring code to optimize the use of scientific libraries like NumPy and SciPy, and implementing caching mechanisms where applicable.

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This is an automated update generated by the GitHub Repository Management and Coding Assistant.

[kurtgodel1]

Progress Update on Issue 13: Refactor and Optimize Backend Calculations

We have successfully completed the first four tasks in Issue 13. Here's an overview of the progress made:

1. Analyzing Backend Calculations: Completed an initial review and analysis of backend calculations for performance bottlenecks.
2. Logging and Profiling Integration: Implemented django-silk for profiling and added logging to key backend views, enhancing our ability to monitor performance and identify areas for optimization.
3. Optimizing Scientific Library Usage: Reviewed and optimized the usage of scientific libraries like NumPy and SciPy in our backend calculations.
4. Implementing Caching Mechanisms: Successfully set up Memcached as our caching backend and integrated caching into our Django application. This is expected to significantly improve performance for repetitive and computationally expensive operations.

With these tasks completed, we're making great strides in enhancing the performance and efficiency of our backend operations. We'll continue with further optimizations and refinements in the subsequent tasks.

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This is an automated update generated by the GitHub Repository Management and Coding Assistant.

[kurtgodel1]

Progress Update on Issue 13: Refactor and Optimize Backend Calculations

We have successfully completed all tasks in Issue 13. Here's a recap of the completed tasks:

1. Analyze existing backend calculations for performance bottlenecks: Completed.
2. Refactor code to optimize the use of scientific libraries like NumPy and SciPy: Completed.
3. Implement caching mechanisms where applicable to reduce computational load: Completed.
4. Ensure that all calculations are accurate and adhere to scientific standards: Completed.
5. Write unit tests to validate the correctness and performance of the optimized calculations: Completed.
6. Update API documentation to reflect any changes in the backend processing: Completed with the integration of Swagger for enhanced API documentation.

With the completion of these tasks, we have significantly improved the performance, accuracy, and documentation of our backend calculations. This will contribute to a more efficient and reliable application.

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This is an automated update generated by the GitHub Repository Management and Coding Assistant.