Files
personal-portfolio/portfolio_app/figures/radar.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

167 lines
4.7 KiB
Python

"""Radar/spider chart figure factory for multi-metric comparison."""
from typing import Any
import plotly.graph_objects as go
def create_radar_figure(
data: list[dict[str, Any]],
metrics: list[str],
name_column: str | None = None,
title: str | None = None,
fill: bool = True,
colors: list[str] | None = None,
) -> go.Figure:
"""Create radar/spider chart for multi-axis comparison.
Each record in data represents one entity (e.g., a neighbourhood)
with values for each metric that will be plotted on a separate axis.
Args:
data: List of data records, each with values for the metrics.
metrics: List of metric column names to display on radar axes.
name_column: Column name for entity labels.
title: Optional chart title.
fill: Whether to fill the radar polygons.
colors: List of colors for each data series.
Returns:
Plotly Figure object.
"""
if not data or not metrics:
return _create_empty_figure(title or "Radar Chart")
# Default colors
if colors is None:
colors = [
"#2196F3",
"#4CAF50",
"#FF9800",
"#E91E63",
"#9C27B0",
"#00BCD4",
]
fig = go.Figure()
# Format axis labels
axis_labels = [m.replace("_", " ").title() for m in metrics]
for i, record in enumerate(data):
values = [record.get(m, 0) or 0 for m in metrics]
# Close the radar polygon
values_closed = values + [values[0]]
labels_closed = axis_labels + [axis_labels[0]]
name = (
record.get(name_column, f"Series {i + 1}")
if name_column
else f"Series {i + 1}"
)
color = colors[i % len(colors)]
fig.add_trace(
go.Scatterpolar(
r=values_closed,
theta=labels_closed,
name=name,
line={"color": color, "width": 2},
fill="toself" if fill else None,
fillcolor=f"rgba{_hex_to_rgba(color, 0.2)}" if fill else None,
hovertemplate="%{theta}: %{r:.1f}<extra></extra>",
)
)
fig.update_layout(
title=title,
polar={
"radialaxis": {
"visible": True,
"gridcolor": "rgba(128,128,128,0.3)",
"linecolor": "rgba(128,128,128,0.3)",
"tickfont": {"color": "#c9c9c9"},
},
"angularaxis": {
"gridcolor": "rgba(128,128,128,0.3)",
"linecolor": "rgba(128,128,128,0.3)",
"tickfont": {"color": "#c9c9c9"},
},
"bgcolor": "rgba(0,0,0,0)",
},
paper_bgcolor="rgba(0,0,0,0)",
font_color="#c9c9c9",
showlegend=len(data) > 1,
legend={"orientation": "h", "yanchor": "bottom", "y": -0.2},
margin={"l": 40, "r": 40, "t": 60, "b": 40},
)
return fig
def create_comparison_radar(
selected_data: dict[str, Any],
average_data: dict[str, Any],
metrics: list[str],
selected_name: str = "Selected",
average_name: str = "City Average",
title: str | None = None,
) -> go.Figure:
"""Create radar chart comparing a selection to city average.
Args:
selected_data: Data for the selected entity.
average_data: Data for the city average.
metrics: List of metric column names.
selected_name: Label for selected entity.
average_name: Label for average.
title: Optional chart title.
Returns:
Plotly Figure object.
"""
if not selected_data or not average_data:
return _create_empty_figure(title or "Comparison")
data = [
{**selected_data, "__name__": selected_name},
{**average_data, "__name__": average_name},
]
return create_radar_figure(
data=data,
metrics=metrics,
name_column="__name__",
title=title,
colors=["#4CAF50", "#9E9E9E"],
)
def _hex_to_rgba(hex_color: str, alpha: float) -> tuple[int, int, int, float]:
"""Convert hex color to RGBA tuple."""
hex_color = hex_color.lstrip("#")
r = int(hex_color[0:2], 16)
g = int(hex_color[2:4], 16)
b = int(hex_color[4:6], 16)
return (r, g, b, alpha)
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)",
font_color="#c9c9c9",
)
return fig