feat: Trust Comparison 6-chart dashboard with real data (Task 10.8)

- Add 3 new visualization functions to plotly_generator.py:
  create_trust_market_share_figure, create_trust_heatmap_figure,
  create_trust_duration_figure
- Replace 6 placeholder callbacks in trust_comparison.py with real
  implementations using trust-comparison queries + figure builders
- Cost Waterfall reuses existing figure function via key mapping
- Dosing reuses existing create_dosing_figure with group_by="trust"
- Cost Effectiveness reuses existing function scoped to directorate
- All 6 charts respond to date filter and chart type toggle
- Validated with both directory (RHEUMATOLOGY) and indication (asthma)
This commit is contained in:
Andrew Charlwood
2026-02-06 22:23:47 +00:00
parent b52fc295de
commit ea6b9065bf
3 changed files with 477 additions and 42 deletions
+165 -36
View File
@@ -91,41 +91,170 @@ def register_trust_comparison_callbacks(app):
else:
return show, hide, ""
# Dashboard chart rendering will be added in Task 10.8.
# For now, register empty figure placeholders for the 6 chart IDs
# so the dcc.Graph components don't error on load.
_tc_chart_ids = [
"tc-chart-market-share",
"tc-chart-cost-waterfall",
"tc-chart-dosing",
"tc-chart-heatmap",
"tc-chart-duration",
"tc-chart-cost-effectiveness",
]
# --- Trust Comparison dashboard charts (6 charts) ---
for chart_id in _tc_chart_ids:
@app.callback(
Output(chart_id, "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
def _tc_empty(message):
"""Return a blank figure with a centered message for TC dashboard."""
fig = go.Figure()
fig.update_layout(
xaxis={"visible": False}, yaxis={"visible": False},
plot_bgcolor="rgba(0,0,0,0)", paper_bgcolor="rgba(0,0,0,0)",
margin={"t": 0, "l": 0, "r": 0, "b": 0}, height=300,
annotations=[{
"text": message, "xref": "paper", "yref": "paper",
"x": 0.5, "y": 0.5, "showarrow": False,
"font": {"size": 14, "color": "#768692", "family": "Source Sans 3"},
"xanchor": "center", "yanchor": "middle",
}],
)
def _placeholder_chart(app_state, _cid=chart_id):
"""Placeholder — returns empty figure until Task 10.8 implements real charts."""
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
fig = go.Figure()
fig.update_layout(
template="plotly_white",
margin=dict(l=20, r=20, t=30, b=20),
height=300,
annotations=[
dict(
text="Chart will be implemented in Task 10.8",
xref="paper", yref="paper",
x=0.5, y=0.5, showarrow=False,
font=dict(size=14, color="#999"),
)
],
)
return fig
return fig
def _tc_title(app_state):
"""Generate a short title suffix from global filter state."""
chart_type = (app_state or {}).get("chart_type", "directory")
label = "By Indication" if chart_type == "indication" else "By Directory"
initiated = (app_state or {}).get("initiated", "all")
last_seen = (app_state or {}).get("last_seen", "6mo")
i_labels = {"all": "All years", "1yr": "Last 1 yr", "2yr": "Last 2 yrs"}
l_labels = {"6mo": "6 mo", "12mo": "12 mo"}
return f"{label} | {i_labels.get(initiated, 'All')} / {l_labels.get(last_seen, '6 mo')}"
# 1. Market Share — drug breakdown per trust
@app.callback(
Output("tc-chart-market-share", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_market_share(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_trust_market_share
from visualization.plotly_generator import create_trust_market_share_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_trust_market_share(filter_id, chart_type, selected)
except Exception:
return _tc_empty("Failed to load market share data.")
if not data:
return _tc_empty("No market share data for this selection.")
return create_trust_market_share_figure(data, _tc_title(app_state))
# 2. Cost Waterfall — cost per patient by trust
@app.callback(
Output("tc-chart-cost-waterfall", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_cost_waterfall(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_trust_cost_waterfall
from visualization.plotly_generator import create_cost_waterfall_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_trust_cost_waterfall(filter_id, chart_type, selected)
except Exception:
return _tc_empty("Failed to load cost data.")
if not data:
return _tc_empty("No cost data for this selection.")
# Reuse existing waterfall figure — map trust_name to directory key
mapped = [{"directory": d["trust_name"], "patients": d["patients"],
"total_cost": d["total_cost"], "cost_pp": d["cost_pp"]} for d in data]
return create_cost_waterfall_figure(mapped, _tc_title(app_state))
# 3. Dosing — drug dosing intervals by trust
@app.callback(
Output("tc-chart-dosing", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_dosing(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_trust_dosing
from visualization.plotly_generator import create_dosing_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_trust_dosing(filter_id, chart_type, selected)
except Exception:
return _tc_empty("Failed to load dosing data.")
if not data:
return _tc_empty("No dosing data for this selection.")
# Add directory field expected by _dosing_by_trust helper
for d in data:
d["directory"] = selected
return create_dosing_figure(data, _tc_title(app_state), group_by="trust")
# 4. Heatmap — trust x drug matrix
@app.callback(
Output("tc-chart-heatmap", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_heatmap(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_trust_heatmap
from visualization.plotly_generator import create_trust_heatmap_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_trust_heatmap(filter_id, chart_type, selected)
except Exception:
return _tc_empty("Failed to load heatmap data.")
if not data.get("trusts") or not data.get("drugs"):
return _tc_empty("No heatmap data for this selection.")
return create_trust_heatmap_figure(data, _tc_title(app_state))
# 5. Duration — drug durations by trust
@app.callback(
Output("tc-chart-duration", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_duration(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_trust_durations
from visualization.plotly_generator import create_trust_duration_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_trust_durations(filter_id, chart_type, selected)
except Exception:
return _tc_empty("Failed to load duration data.")
if not data:
return _tc_empty("No duration data for this selection.")
return create_trust_duration_figure(data, _tc_title(app_state))
# 6. Cost Effectiveness — pathway costs within directorate (NOT split by trust)
@app.callback(
Output("tc-chart-cost-effectiveness", "figure"),
Input("app-state", "data"),
prevent_initial_call=True,
)
def tc_cost_effectiveness(app_state):
selected = (app_state or {}).get("selected_comparison_directorate")
if not selected:
return no_update
from dash_app.data.queries import get_pathway_costs
from data_processing.parsing import calculate_retention_rate
from visualization.plotly_generator import create_cost_effectiveness_figure
filter_id = app_state.get("date_filter_id", "all_6mo")
chart_type = app_state.get("chart_type", "directory")
try:
data = get_pathway_costs(filter_id, chart_type, directory=selected)
except Exception:
return _tc_empty("Failed to load pathway cost data.")
if not data:
return _tc_empty("No pathway cost data for this selection.")
retention = calculate_retention_rate(data)
return create_cost_effectiveness_figure(data, retention, _tc_title(app_state))