Removed unnecessary imports across various modules to streamline the application's dependencies and improve loading times. Specific changes include the removal of unused Django model and admin imports in several apps, simplifying view imports by eliminating unutilized components, and cleaning up static CSS for better maintainability. Corrections were made to conditional expressions for clearer logic. The removal of the django.test.TestCase import in test files reflects a shift towards a different testing strategy or the current lack of tests. Exception handling has been made more explicit to avoid catching unintended exceptions, paving the way for more robust error handling and logging in the future. Additionally, a new CSS file was added for frontend enhancements, indicating ongoing UI/UX improvements.
These changes collectively aim to make the codebase more maintainable and efficient, reducing clutter and focusing on used functionalities. It's a step towards optimizing the application's performance and ensuring a clean, manageable codebase for future development.
Reinforced user data access rules to bolster security and reorganized distribution files into separate directories for cleaner structure. Added a new heatmap visualization for mood statistics on the dashboard, making user engagements more interactive and insightful. Implemented a JSON view to support the heatmap feature, fetching mood entries within a specified time range.
This change responds to the need for improved data security and a more engaging user interface, directly addressing user feedback for clearer insights into their mood patterns over time.
Updated the property for setting the chart legend's label from `legend`
to `legend_label` in the `moodpies` function to align with the latest
library syntax. This change ensures compatibility with newer versions of
the visualization library, preventing potential issues with legend
rendering in mood statistics charts.
This commit streamlines mood and activity visualization code in
mood/statistics.py for better readability and maintainability. By
consolidating similar code blocks, replacing ' extension calls with
consistent double quotes, and reformatting large data structures for
clarity, we ensure the code is more Pythonic and easier to follow.
Additionally, the alterations in how pie charts and mood statistics are
generated not only maintain functionality but also reduce cognitive load
when navigating the codebase. These changes pave the way for future
enhancements and debugging efforts by making the codebase more
approachable.
Standardized the parameter name for setting chart height across all
chart creation functions in statistics.py. Replaced `plot_height` with
`height` to align with the latest visualization library conventions.
This change enhances code consistency and adheres to the updated library
API, ensuring future compatibility and easier maintenance.
