Files
personal-portfolio/portfolio_app/figures/scatter.py
lmiranda c9cf744d84 feat: Complete Phase 5 dashboard implementation
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>
2026-01-17 11:46:18 -05:00

185 lines
4.9 KiB
Python

"""Scatter plot figure factory for correlation views."""
from typing import Any
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
def create_scatter_figure(
data: list[dict[str, Any]],
x_column: str,
y_column: str,
name_column: str | None = None,
size_column: str | None = None,
color_column: str | None = None,
title: str | None = None,
x_title: str | None = None,
y_title: str | None = None,
trendline: bool = False,
color_scale: str = "Blues",
) -> go.Figure:
"""Create scatter plot for correlation visualization.
Args:
data: List of data records.
x_column: Column name for x-axis values.
y_column: Column name for y-axis values.
name_column: Column name for point labels (hover).
size_column: Column name for point sizes.
color_column: Column name for color encoding.
title: Optional chart title.
x_title: X-axis title.
y_title: Y-axis title.
trendline: Whether to add OLS trendline.
color_scale: Plotly color scale for continuous colors.
Returns:
Plotly Figure object.
"""
if not data:
return _create_empty_figure(title or "Scatter Plot")
df = pd.DataFrame(data)
# Build hover_data
hover_data = {}
if name_column and name_column in df.columns:
hover_data[name_column] = True
# Create scatter plot
fig = px.scatter(
df,
x=x_column,
y=y_column,
size=size_column if size_column and size_column in df.columns else None,
color=color_column if color_column and color_column in df.columns else None,
color_continuous_scale=color_scale,
hover_name=name_column,
trendline="ols" if trendline else None,
opacity=0.7,
)
# Style the markers
fig.update_traces(
marker={
"line": {"width": 1, "color": "rgba(255,255,255,0.3)"},
},
)
# Trendline styling
if trendline:
fig.update_traces(
selector={"mode": "lines"},
line={"color": "#FF9800", "dash": "dash", "width": 2},
)
fig.update_layout(
title=title,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="#c9c9c9",
xaxis={
"gridcolor": "rgba(128,128,128,0.2)",
"title": x_title or x_column.replace("_", " ").title(),
"zeroline": False,
},
yaxis={
"gridcolor": "rgba(128,128,128,0.2)",
"title": y_title or y_column.replace("_", " ").title(),
"zeroline": False,
},
margin={"l": 10, "r": 10, "t": 40, "b": 10},
showlegend=color_column is not None,
)
return fig
def create_bubble_chart(
data: list[dict[str, Any]],
x_column: str,
y_column: str,
size_column: str,
name_column: str | None = None,
color_column: str | None = None,
title: str | None = None,
x_title: str | None = None,
y_title: str | None = None,
size_max: int = 50,
) -> go.Figure:
"""Create bubble chart with sized markers.
Args:
data: List of data records.
x_column: Column name for x-axis values.
y_column: Column name for y-axis values.
size_column: Column name for bubble sizes.
name_column: Column name for labels.
color_column: Column name for colors.
title: Optional chart title.
x_title: X-axis title.
y_title: Y-axis title.
size_max: Maximum marker size in pixels.
Returns:
Plotly Figure object.
"""
if not data:
return _create_empty_figure(title or "Bubble Chart")
df = pd.DataFrame(data)
fig = px.scatter(
df,
x=x_column,
y=y_column,
size=size_column,
color=color_column,
hover_name=name_column,
size_max=size_max,
opacity=0.7,
)
fig.update_layout(
title=title,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="#c9c9c9",
xaxis={
"gridcolor": "rgba(128,128,128,0.2)",
"title": x_title or x_column.replace("_", " ").title(),
},
yaxis={
"gridcolor": "rgba(128,128,128,0.2)",
"title": y_title or y_column.replace("_", " ").title(),
},
margin={"l": 10, "r": 10, "t": 40, "b": 10},
)
return fig
def _create_empty_figure(title: str) -> go.Figure:
"""Create an empty figure with a message."""
fig = go.Figure()
fig.add_annotation(
text="No data available",
xref="paper",
yref="paper",
x=0.5,
y=0.5,
showarrow=False,
font={"size": 14, "color": "#888888"},
)
fig.update_layout(
title=title,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="#c9c9c9",
xaxis={"visible": False},
yaxis={"visible": False},
)
return fig