feat: implement chart data preparation (Task 4.1)
- Add prepare_chart_data() method for hierarchical chart data - Build Trust → Directory → Drug hierarchy from filtered SQLite data - Calculate patient counts and costs at each hierarchy level - Compute color values (proportions) for visualization - Generate dynamic chart title based on filter state - Call prepare_chart_data() from apply_filters() for reactivity - Mark Task 3.4 complete (KPIs implemented in apply_filters)
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Andrew Charlwood
+11
-6
@@ -129,17 +129,22 @@ cd pathways_app && timeout 60 python -m reflex run 2>&1 | head -30
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- [x] Return filtered DataFrame
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### 3.4 KPI Calculations
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- [ ] Create computed properties for KPI values:
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- [x] Create computed properties for KPI values:
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- `unique_patients: int` — COUNT(DISTINCT patient_id) from filtered data
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- (Future: drug count, total cost, indication match rate)
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- [ ] Ensure KPIs update reactively when filters change
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- `total_drugs: int` — COUNT(DISTINCT drug_name_std) from filtered data
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- `total_cost: float` — SUM(price_actual) from filtered data
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- (Blocked: indication_match_rate requires Snowflake GP data)
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- [x] Ensure KPIs update reactively when filters change
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- Note: KPIs implemented in apply_filters() method, called by all filter event handlers
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## Phase 4: Interactive Chart
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### 4.1 Chart Data Preparation
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- [ ] Create `prepare_chart_data()` method that transforms filtered data for Plotly icicle
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- [ ] Reuse/adapt logic from existing `pathway_analyzer.py`
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- [ ] Return data structure compatible with `plotly.express.icicle()`
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- [x] Create `prepare_chart_data()` method that transforms filtered data for Plotly icicle
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- [x] Reuse/adapt logic from existing `pathway_analyzer.py` (simplified hierarchy: Trust → Directory → Drug)
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- [x] Return data structure compatible with `go.Icicle()` (list of dicts with parents, ids, labels, value, cost, colour)
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- [x] Generate chart_title based on current filter state
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- [x] Call prepare_chart_data() from apply_filters() for reactive updates
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### 4.2 Reactive Plotly Integration
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- [ ] Create `generate_icicle_chart()` computed property that returns Plotly figure
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@@ -509,6 +509,9 @@ class AppState(rx.State):
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conn.close()
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self.error_message = ""
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# Update chart data with new filtered results
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self.prepare_chart_data()
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except sqlite3.Error as e:
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self.error_message = f"Database error: {str(e)}"
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except Exception as e:
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@@ -628,6 +631,278 @@ class AppState(rx.State):
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self.error_message = f"Failed to load data: {str(e)}"
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self.data_loaded = False
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# =========================================================================
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# Chart Data Preparation Methods
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# =========================================================================
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# Chart data stored as list of dicts for Reflex serialization
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# Structure: [{"parents": str, "ids": str, "labels": str, "value": int, "cost": float, "colour": float}, ...]
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chart_data: list[dict[str, Any]] = []
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chart_title: str = ""
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def prepare_chart_data(self):
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"""
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Prepare hierarchical data for Plotly icicle chart.
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This method queries the filtered patient data and transforms it into
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a hierarchical structure: Root → Trust → Directory → Drug
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The chart data is stored in self.chart_data as a list of dicts with:
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- parents: Parent node identifier
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- ids: Unique node identifier (hierarchical path)
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- labels: Display label
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- value: Patient count
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- cost: Total cost
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- colour: Color value (proportion of parent)
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Updates: chart_data, chart_title, chart_loading
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"""
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import sqlite3
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db_path = Path("data/pathways.db")
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if not db_path.exists():
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self.error_message = "Database not found."
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self.chart_data = []
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return
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self.chart_loading = True
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try:
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conn = sqlite3.connect(str(db_path))
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cursor = conn.cursor()
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# Build WHERE clause for filters
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where_clauses = []
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params = []
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# Drug filter (if any drugs selected)
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if self.selected_drugs:
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placeholders = ",".join("?" * len(self.selected_drugs))
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where_clauses.append(f"drug_name_std IN ({placeholders})")
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params.extend(self.selected_drugs)
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# Directorate filter (if any directorates selected)
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if self.selected_directorates:
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placeholders = ",".join("?" * len(self.selected_directorates))
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where_clauses.append(f"directory IN ({placeholders})")
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params.extend(self.selected_directorates)
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base_where = ""
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if where_clauses:
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base_where = "WHERE " + " AND ".join(where_clauses)
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# Build date filter HAVING clauses for patient-level filtering
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having_clauses = []
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having_params = []
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if self.initiated_filter_enabled and self.initiated_from_date:
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having_clauses.append("first_seen >= ?")
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having_params.append(self.initiated_from_date)
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if self.initiated_filter_enabled and self.initiated_to_date:
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having_clauses.append("first_seen <= ?")
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having_params.append(self.initiated_to_date)
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if self.last_seen_filter_enabled and self.last_seen_from_date:
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having_clauses.append("last_seen >= ?")
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having_params.append(self.last_seen_from_date)
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if self.last_seen_filter_enabled and self.last_seen_to_date:
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having_clauses.append("last_seen <= ?")
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having_params.append(self.last_seen_to_date)
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having_clause = ""
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if having_clauses:
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having_clause = "HAVING " + " AND ".join(having_clauses)
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# Query to get aggregated data by Trust -> Directory -> Drug
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# fact_interventions already has org_name, use it directly
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chart_query = f"""
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WITH filtered_patients AS (
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SELECT upid
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FROM (
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SELECT
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upid,
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MIN(intervention_date) as first_seen,
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MAX(intervention_date) as last_seen
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FROM fact_interventions
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{base_where}
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GROUP BY upid
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{having_clause}
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)
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),
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patient_records AS (
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SELECT
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f.upid,
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COALESCE(f.org_name, f.provider_code) as trust_name,
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f.directory,
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f.drug_name_std,
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f.price_actual
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FROM fact_interventions f
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INNER JOIN filtered_patients fp ON f.upid = fp.upid
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{base_where.replace('WHERE', 'AND') if base_where else ''}
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)
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SELECT
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trust_name,
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directory,
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drug_name_std,
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COUNT(DISTINCT upid) as patient_count,
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COALESCE(SUM(price_actual), 0) as total_cost
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FROM patient_records
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GROUP BY trust_name, directory, drug_name_std
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ORDER BY trust_name, directory, drug_name_std
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"""
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all_params = params + having_params
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if where_clauses:
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all_params.extend(params)
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cursor.execute(chart_query, all_params)
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rows = cursor.fetchall()
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conn.close()
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# Build hierarchical chart data
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chart_data = []
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hierarchy_totals = {} # Track totals for calculating color values
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# Root node
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root_id = "N&WICS"
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chart_data.append({
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"parents": "",
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"ids": root_id,
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"labels": "Norfolk & Waveney ICS",
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"value": 0,
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"cost": 0.0,
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"colour": 1.0,
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})
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# Process rows to build hierarchy
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trust_totals = {}
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directory_totals = {}
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drug_data = []
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for row in rows:
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trust_name, directory, drug_name, patient_count, cost = row
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if not trust_name or not directory or not drug_name:
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continue
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# Trust level
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trust_id = f"{root_id} - {trust_name}"
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if trust_id not in trust_totals:
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trust_totals[trust_id] = {"value": 0, "cost": 0.0, "label": trust_name}
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trust_totals[trust_id]["value"] += patient_count
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trust_totals[trust_id]["cost"] += cost
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# Directory level
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dir_id = f"{trust_id} - {directory}"
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if dir_id not in directory_totals:
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directory_totals[dir_id] = {
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"value": 0,
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"cost": 0.0,
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"label": directory,
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"parent": trust_id,
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}
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directory_totals[dir_id]["value"] += patient_count
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directory_totals[dir_id]["cost"] += cost
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# Drug level (leaf)
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drug_id = f"{dir_id} - {drug_name}"
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drug_data.append({
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"ids": drug_id,
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"labels": drug_name,
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"parent": dir_id,
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"value": patient_count,
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"cost": float(cost),
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})
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# Calculate root total
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root_total = sum(t["value"] for t in trust_totals.values())
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root_cost = sum(t["cost"] for t in trust_totals.values())
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chart_data[0]["value"] = root_total
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chart_data[0]["cost"] = root_cost
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# Add trust nodes with color proportions
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for trust_id, data in trust_totals.items():
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colour = data["value"] / root_total if root_total > 0 else 0
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chart_data.append({
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"parents": root_id,
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"ids": trust_id,
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"labels": data["label"],
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"value": data["value"],
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"cost": data["cost"],
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"colour": colour,
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})
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# Add directory nodes with color proportions
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for dir_id, data in directory_totals.items():
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parent_total = trust_totals[data["parent"]]["value"]
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colour = data["value"] / parent_total if parent_total > 0 else 0
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chart_data.append({
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"parents": data["parent"],
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"ids": dir_id,
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"labels": data["label"],
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"value": data["value"],
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"cost": data["cost"],
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"colour": colour,
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})
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# Add drug nodes with color proportions
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for drug in drug_data:
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parent_dir = drug["parent"]
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parent_total = directory_totals[parent_dir]["value"]
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colour = drug["value"] / parent_total if parent_total > 0 else 0
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chart_data.append({
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"parents": parent_dir,
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"ids": drug["ids"],
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"labels": drug["labels"],
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"value": drug["value"],
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"cost": drug["cost"],
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"colour": colour,
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})
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self.chart_data = chart_data
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self.chart_title = self._generate_chart_title()
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self.chart_loading = False
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self.error_message = ""
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except sqlite3.Error as e:
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self.error_message = f"Chart data error: {str(e)}"
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self.chart_data = []
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self.chart_loading = False
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except Exception as e:
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self.error_message = f"Chart preparation failed: {str(e)}"
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self.chart_data = []
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self.chart_loading = False
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def _generate_chart_title(self) -> str:
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"""Generate chart title based on current filter state."""
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parts = []
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# Date range info
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if self.last_seen_filter_enabled:
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parts.append(f"Last seen: {self.last_seen_from_date} to {self.last_seen_to_date}")
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elif self.initiated_filter_enabled:
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parts.append(f"Initiated: {self.initiated_from_date} to {self.initiated_to_date}")
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# Drug selection info
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if self.selected_drugs:
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if len(self.selected_drugs) <= 3:
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parts.append(", ".join(self.selected_drugs))
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else:
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parts.append(f"{len(self.selected_drugs)} drugs selected")
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# Directorate selection info
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if self.selected_directorates:
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if len(self.selected_directorates) <= 2:
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parts.append(", ".join(self.selected_directorates))
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else:
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parts.append(f"{len(self.selected_directorates)} directorates")
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if parts:
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return " | ".join(parts)
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return "All Patients"
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# =============================================================================
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# Layout Components
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