lmiranda
c9cf744d84
Implement full 5-tab Toronto Neighbourhood Dashboard with real data connectivity: Dashboard Structure: - Overview tab with livability scores and rankings - Housing tab with affordability metrics - Safety tab with crime statistics - Demographics tab with population/income data - Amenities tab with parks, schools, transit Figure Factories (portfolio_app/figures/): - bar_charts.py: ranking, stacked, horizontal bars - scatter.py: scatter plots, bubble charts - radar.py: spider/radar charts - demographics.py: donut, age pyramid, income distribution Service Layer (portfolio_app/toronto/services/): - neighbourhood_service.py: queries dbt marts for all tab data - geometry_service.py: generates GeoJSON from PostGIS - Graceful error handling when database unavailable Callbacks (portfolio_app/pages/toronto/callbacks/): - map_callbacks.py: choropleth updates, map click handling - chart_callbacks.py: supporting chart updates - selection_callbacks.py: dropdown handlers, KPI updates Data Pipeline (scripts/data/): - load_toronto_data.py: orchestration script with CLI flags Lessons Learned: - Graceful error handling in service layers - Modular callback structure for multi-tab dashboards - Figure factory pattern for reusable charts Closes: #64, #65, #66, #67, #68, #69, #70 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
46 lines
1.6 KiB
Markdown
46 lines
1.6 KiB
Markdown
# Sprint 9-10 - Modular Callback Structure for Multi-Tab Dashboards
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## Context
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Implementing a 5-tab Toronto Neighbourhood Dashboard with multiple callbacks per tab (map updates, chart updates, KPI updates, selection handling).
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## Problem
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Initial callback implementation approach would have placed all callbacks in a single file, leading to:
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- A monolithic file with 500+ lines
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- Difficult-to-navigate code
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- Callbacks for different tabs interleaved
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- Testing difficulties
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## Solution
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Organized callbacks into three focused modules:
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```
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callbacks/
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├── __init__.py # Imports all modules to register callbacks
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├── map_callbacks.py # Choropleth updates, map click handling
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├── chart_callbacks.py # Supporting chart updates (scatter, trend, donut)
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└── selection_callbacks.py # Dropdown population, KPI updates
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```
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Key patterns:
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1. **Group by responsibility**, not by tab - all map-related callbacks together
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2. **Use noqa comments** for imports that register callbacks as side effects
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3. **Share helper functions** (like `_empty_chart()`) within modules
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```python
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# callbacks/__init__.py
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from . import (
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chart_callbacks, # noqa: F401
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map_callbacks, # noqa: F401
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selection_callbacks, # noqa: F401
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)
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```
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## Prevention
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- **Plan callback organization before implementation** - sketch which callbacks go where
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- **Group by function, not by feature** - keeps related logic together
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- **Keep modules under 400 lines** - split if exceeding
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- **Test imports early** - verify callbacks register correctly
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## Tags
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dash, callbacks, architecture, python, code-organization, maintainability
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