Introduces NavCollapse to manage grouped navigation items.
Simplifies 'features.py' by organizing items under collapsible sections.
Updates HTML generation for both NavItem and NavCollapse classes.
Improves user experience by improving sidebar organization.
Introduces DashboardSection class to encapsulate dashboard logic,
enabling easier extension and customization. Adds separate mood
and dreams dashboard templates to streamline content rendering.
Refactors main dashboard template to dynamically render content
from enabled modules using a new template tag system, improving
flexibility and maintainability.
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.
This commit establishes the initial set of models required for the dream tracking feature within the application, including models for Dream, DreamMedia, Theme, DreamTheme, and ThemeRating, along with their associations. The structure allows for comprehensive tracking and categorization of user dreams, media associated with dreams, thematic elements, and ratings for these themes. This foundational work is critical for enabling detailed dream recording and analysis, supporting functionalities such as dream categorization, mood association, and user-specific customization.
The relationships between models facilitate advanced queries for insights and trends in dream content, mood correlations, and thematic popularity. The choice of fields and types ensures a balance between flexibility for future enhancements and the current performance requirements.
