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chore: Remove TRREB references from Python modules

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- Remove DimTRREBDistrict model and FactPurchases model
- Remove TRREBDistrict schema and AreaType enum
- Remove TRREBDistrictParser from geo parsers
- Remove load_trreb_districts from dimension loaders
- Remove create_district_map from choropleth figures
- Remove get_demo_districts and get_demo_purchase_data from demo_data
- Update summary metrics to remove purchase-related metrics
- Update callbacks to remove TRREB-related comments
- Update methodology page to remove TRREB data source section
- Update dashboard data notice to remove TRREB mention

Closes #49

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Leo Miranda
2026-01-16 10:11:11 -05:00
parent cb877df9e1
commit fcaefabce8
15 changed files with 16 additions and 464 deletions
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109
portfolio_app/toronto/demo_data.py
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@@ -8,98 +8,6 @@ from datetime import date
from typing import Any
def get_demo_districts() -> list[dict[str, Any]]:
"""Return sample TRREB district data."""
return [
{"district_code": "W01", "district_name": "Long Branch", "area_type": "West"},
{"district_code": "W02", "district_name": "Mimico", "area_type": "West"},
{
"district_code": "W03",
"district_name": "Kingsway South",
"area_type": "West",
},
{"district_code": "W04", "district_name": "Edenbridge", "area_type": "West"},
{"district_code": "W05", "district_name": "Islington", "area_type": "West"},
{"district_code": "W06", "district_name": "Rexdale", "area_type": "West"},
{"district_code": "W07", "district_name": "Willowdale", "area_type": "West"},
{"district_code": "W08", "district_name": "York", "area_type": "West"},
{
"district_code": "C01",
"district_name": "Downtown Core",
"area_type": "Central",
},
{"district_code": "C02", "district_name": "Annex", "area_type": "Central"},
{
"district_code": "C03",
"district_name": "Forest Hill",
"area_type": "Central",
},
{
"district_code": "C04",
"district_name": "Lawrence Park",
"area_type": "Central",
},
{
"district_code": "C06",
"district_name": "Willowdale East",
"area_type": "Central",
},
{"district_code": "C07", "district_name": "Thornhill", "area_type": "Central"},
{"district_code": "C08", "district_name": "Waterfront", "area_type": "Central"},
{"district_code": "E01", "district_name": "Leslieville", "area_type": "East"},
{"district_code": "E02", "district_name": "The Beaches", "area_type": "East"},
{"district_code": "E03", "district_name": "Danforth", "area_type": "East"},
{"district_code": "E04", "district_name": "Birch Cliff", "area_type": "East"},
{"district_code": "E05", "district_name": "Scarborough", "area_type": "East"},
]
def get_demo_purchase_data() -> list[dict[str, Any]]:
"""Return sample purchase data for time series visualization."""
import random
random.seed(42)
data = []
base_prices = {
"W01": 850000,
"C01": 1200000,
"E01": 950000,
}
for year in [2024, 2025]:
for month in range(1, 13):
if year == 2025 and month > 12:
break
for district, base_price in base_prices.items():
# Add some randomness and trend
trend = (year - 2024) * 12 + month
price_variation = random.uniform(-0.05, 0.05)
trend_factor = 1 + (trend * 0.002) # Slight upward trend
avg_price = int(base_price * trend_factor * (1 + price_variation))
sales = random.randint(50, 200)
data.append(
{
"district_code": district,
"full_date": date(year, month, 1),
"year": year,
"month": month,
"avg_price": avg_price,
"median_price": int(avg_price * 0.95),
"sales_count": sales,
"new_listings": int(sales * random.uniform(1.2, 1.8)),
"active_listings": int(sales * random.uniform(2.0, 3.5)),
"days_on_market": random.randint(15, 45),
"sale_to_list_ratio": round(random.uniform(0.95, 1.05), 2),
}
)
return data
def get_demo_rental_data() -> list[dict[str, Any]]:
"""Return sample rental data for visualization."""
data = []
@@ -219,23 +127,6 @@ def get_demo_policy_events() -> list[dict[str, Any]]:
def get_demo_summary_metrics() -> dict[str, dict[str, Any]]:
"""Return summary metrics for KPI cards."""
return {
"avg_price": {
"value": 1067968,
"title": "Avg. Price (2025)",
"delta": -4.7,
"delta_suffix": "%",
"prefix": "$",
"format_spec": ",.0f",
"positive_is_good": True,
},
"total_sales": {
"value": 67610,
"title": "Total Sales (2024)",
"delta": 2.6,
"delta_suffix": "%",
"format_spec": ",.0f",
"positive_is_good": True,
},
"avg_rent": {
"value": 2450,
"title": "Avg. Rent (2025)",