Corrects the timestamp comparison in the status filtering logic
by using the adjusted datetime object for accurate filtering,
ensuring list results reflect intended timestamp boundaries.
Improves calculation of average mood by counting valid mood
entries (moodcount) separately, ensuring division occurs only
on valid mood data. Additionally, refines queryset management
for better readability and consistency, ensuring accurate date
range filters.
This change enhances data accuracy and code maintainability.
Enhances `StatusListView` to support filtering based on
a date range by adding 'from' and 'to' query parameters.
This enables users to view status updates within a specified
date range, improving data accessibility.
Implements click event on calendar heatmap to redirect to
filtered mood page, supporting targeted data exploration.
Ensures that the end date includes the entire day by setting
the time to 23:59:59 when a specific end date is provided.
This avoids excluding entries from the last day in the range.
Adds additional scripts for tooltips and legends in the heatmap,
enhancing data visualization and usability.
Refactors dashboard logic to dynamically fetch and render mood
data, allowing for more customized color scales and average
mood calculations.
Introduces a new API endpoint to provide mood value details,
necessary for proper heatmap rendering.
Improves the handling of mood data to compute averages for each
day, allowing for richer insights into mood patterns.
Updates script and style paths to use Django's static() function
for better path resolution and management. This ensures that
the paths are correctly resolved relative to the static files
configuration, enhancing maintainability and reliability.
Updates the import path for the static method to reflect
recent changes in Django's module structure, ensuring
compatibility with newer versions.
This change helps prevent deprecation issues under
up-to-date Django setups.
Changes hardcoded script URL paths to use Django's static function for better resource management and maintainability.
Improves code readability by breaking lines, adopting PEP 8 style guidelines.
Simplifies model imports and uses consistent string formatting for various UI elements.
Replaces count annotation with custom aggregation logic to
correctly count status entries per date in heatmap data.
Improves data processing by calculating occurrences manually.
Removes previous annotation and modifies output format.
This ensures data accuracy before encoding into JSON response.
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.
