feat: add 7 analytics chart query functions (Task 9.2)
New query functions in src/data_processing/pathway_queries.py: - get_drug_market_share: Level 3 drug nodes grouped by directory - get_pathway_costs: Level 4+ pathway nodes with cost_pp_pa - get_cost_waterfall: Directorate cost per patient from level 3 aggregation - get_drug_transitions: Sankey source/target drug transitions with ordinal line labels - get_dosing_intervals: Parsed average_spacing by trust/directory - get_drug_directory_matrix: Directory x drug pivot with patient/cost metrics - get_treatment_durations: Weighted avg_days by drug within directorates Thin wrappers added in dash_app/data/queries.py for all 7 functions.
This commit is contained in:
Andrew Charlwood
@@ -359,7 +359,7 @@ Drawer selection → update_drug_selection → app-state store → load_pathway_
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- **Checkpoint**: App starts, tab bar renders with all 8 tabs, icicle tab still works, other tabs show placeholder "Coming soon" messages ✓
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### 9.2 Query functions for all chart types
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- [ ] Add to `src/data_processing/pathway_queries.py`:
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- [x] Add to `src/data_processing/pathway_queries.py`:
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- `get_drug_market_share(db_path, date_filter_id, chart_type, directory=None, trust=None)` — Level 3 nodes grouped by directory, returning drug, value, colour
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- `get_pathway_costs(db_path, date_filter_id, chart_type, directory=None)` — Level 4+ nodes with cost_pp_pa, parsed pathway labels, patient counts
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- `get_cost_waterfall(db_path, date_filter_id, chart_type, trust=None)` — Level 2 nodes with cost_pp_pa per directorate/indication
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@@ -367,8 +367,8 @@ Drawer selection → update_drug_selection → app-state store → load_pathway_
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- `get_dosing_intervals(db_path, date_filter_id, chart_type, drug=None)` — Level 3 nodes for a specific drug, parsed average_spacing by trust/directory
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- `get_drug_directory_matrix(db_path, date_filter_id, chart_type)` — Level 3 nodes pivoted as directory × drug with value/cost metrics
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- `get_treatment_durations(db_path, date_filter_id, chart_type, directory=None)` — Level 3 nodes with avg_days by drug within a directorate
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- [ ] Add thin wrappers in `dash_app/data/queries.py` for each new function (resolve DB_PATH and delegate)
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- **Checkpoint**: All 7 query functions return correct data via manual Python tests (`python -c "..."`)
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- [x] Add thin wrappers in `dash_app/data/queries.py` for each new function (resolve DB_PATH and delegate)
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- **Checkpoint**: All 7 query functions return correct data via manual Python tests (`python -c "..."`) ✓
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### 9.3 First-Line Market Share chart (Tab 2)
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- [ ] Create `dash_app/callbacks/market_share.py`:
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@@ -11,6 +11,13 @@ from typing import Optional
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from data_processing.pathway_queries import (
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load_initial_data as _load_initial_data,
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load_pathway_nodes as _load_pathway_nodes,
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get_drug_market_share as _get_drug_market_share,
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get_pathway_costs as _get_pathway_costs,
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get_cost_waterfall as _get_cost_waterfall,
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get_drug_transitions as _get_drug_transitions,
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get_dosing_intervals as _get_dosing_intervals,
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get_drug_directory_matrix as _get_drug_directory_matrix,
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get_treatment_durations as _get_treatment_durations,
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)
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DB_PATH = Path(__file__).resolve().parents[2] / "data" / "pathways.db"
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@@ -37,3 +44,74 @@ def load_pathway_data(
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selected_directorates=selected_directorates,
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selected_trusts=selected_trusts,
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)
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# --- Analytics chart query wrappers (Phase 9) ---
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def get_drug_market_share(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 3 drug nodes grouped by directory with patient counts."""
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return _get_drug_market_share(DB_PATH, date_filter_id, chart_type, directory, trust)
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def get_pathway_costs(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 4+ pathway nodes with annualized cost."""
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return _get_pathway_costs(DB_PATH, date_filter_id, chart_type, directory, trust)
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def get_cost_waterfall(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 2 directorate nodes with cost per patient."""
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return _get_cost_waterfall(DB_PATH, date_filter_id, chart_type, trust)
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def get_drug_transitions(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> dict:
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"""Drug transition data for Sankey diagram."""
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return _get_drug_transitions(DB_PATH, date_filter_id, chart_type, directory, trust)
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def get_dosing_intervals(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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drug: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Dosing interval data parsed from average_spacing."""
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return _get_dosing_intervals(DB_PATH, date_filter_id, chart_type, drug, trust)
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def get_drug_directory_matrix(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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trust: Optional[str] = None,
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) -> dict:
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"""Directory × drug matrix with patient counts and costs."""
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return _get_drug_directory_matrix(DB_PATH, date_filter_id, chart_type, trust)
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def get_treatment_durations(
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date_filter_id: str = "all_6mo",
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chart_type: str = "directory",
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Treatment duration data (avg_days) by drug."""
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return _get_treatment_durations(DB_PATH, date_filter_id, chart_type, directory, trust)
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@@ -330,3 +330,480 @@ def _empty_result(error: str = "") -> dict:
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"last_updated": "",
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"error": error,
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}
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# ---------------------------------------------------------------------------
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# Analytics chart query functions (Phase 9)
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# ---------------------------------------------------------------------------
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def _safe_float(value, default=0.0):
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"""Convert a value to float, returning default for None/N/A/empty."""
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if value is None or value == "" or value == "N/A":
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return default
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try:
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return float(value)
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except (ValueError, TypeError):
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return default
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def get_drug_market_share(
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db_path: Path,
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date_filter_id: str,
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chart_type: str,
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 3 drug nodes grouped by directory with patient counts and proportions.
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Returns list of dicts: [{directory, drug, patients, proportion, cost, cost_pp_pa}]
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Sorted by directory total patients desc, then drug patients desc within each.
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"""
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conn = sqlite3.connect(str(db_path))
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conn.row_factory = sqlite3.Row
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try:
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where = ["date_filter_id = ?", "chart_type = ?", "level = 3"]
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params: list = [date_filter_id, chart_type]
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if directory:
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where.append("directory = ?")
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params.append(directory)
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if trust:
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where.append("trust_name = ?")
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params.append(trust)
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query = f"""
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SELECT labels AS drug, directory, value AS patients,
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colour AS proportion, cost, cost_pp_pa, trust_name
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FROM pathway_nodes
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WHERE {' AND '.join(where)}
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ORDER BY directory, value DESC
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"""
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rows = conn.execute(query, params).fetchall()
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# Aggregate across trusts: sum patients/cost per directory+drug
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agg = {}
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for r in rows:
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key = (r["directory"], r["drug"])
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if key not in agg:
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agg[key] = {"directory": r["directory"], "drug": r["drug"],
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"patients": 0, "cost": 0.0}
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agg[key]["patients"] += r["patients"] or 0
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agg[key]["cost"] += float(r["cost"]) if r["cost"] else 0.0
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# Compute proportions within each directory
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dir_totals = {}
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for v in agg.values():
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dir_totals[v["directory"]] = dir_totals.get(v["directory"], 0) + v["patients"]
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result = []
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for v in agg.values():
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total = dir_totals.get(v["directory"], 1)
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result.append({
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"directory": v["directory"],
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"drug": v["drug"],
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"patients": v["patients"],
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"proportion": round(v["patients"] / total, 4) if total else 0,
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"cost": round(v["cost"], 2),
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"cost_pp_pa": round(v["cost"] / v["patients"], 2) if v["patients"] else 0,
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})
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# Sort: directory by total patients desc, drugs by patients desc within
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result.sort(key=lambda x: (-dir_totals.get(x["directory"], 0), -x["patients"]))
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return result
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except sqlite3.Error:
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return []
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finally:
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conn.close()
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def get_pathway_costs(
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db_path: Path,
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date_filter_id: str,
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chart_type: str,
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 4+ pathway nodes with annualized cost and pathway labels.
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Returns list of dicts: [{ids, pathway_label, cost_pp_pa, patients, directory, drug_sequence}]
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Sorted by cost_pp_pa desc.
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"""
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conn = sqlite3.connect(str(db_path))
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conn.row_factory = sqlite3.Row
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try:
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where = ["date_filter_id = ?", "chart_type = ?", "level >= 4"]
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params: list = [date_filter_id, chart_type]
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if directory:
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where.append("directory = ?")
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params.append(directory)
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if trust:
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where.append("trust_name = ?")
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params.append(trust)
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query = f"""
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SELECT ids, labels, level, value AS patients, cost, costpp,
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cost_pp_pa, avg_days, directory, drug_sequence, trust_name
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FROM pathway_nodes
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WHERE {' AND '.join(where)}
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ORDER BY value DESC
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"""
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rows = conn.execute(query, params).fetchall()
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result = []
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for r in rows:
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cpp = _safe_float(r["cost_pp_pa"])
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drugs = (r["drug_sequence"] or "").split("|")
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pathway_label = " → ".join(d for d in drugs if d)
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result.append({
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"ids": r["ids"],
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"pathway_label": pathway_label,
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"cost_pp_pa": cpp,
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"patients": r["patients"] or 0,
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"cost": float(r["cost"]) if r["cost"] else 0.0,
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"avg_days": _safe_float(r["avg_days"]),
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"directory": r["directory"] or "",
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"trust_name": r["trust_name"] or "",
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"drug_sequence": drugs,
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"level": r["level"],
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})
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result.sort(key=lambda x: -x["cost_pp_pa"])
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return result
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except sqlite3.Error:
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return []
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finally:
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conn.close()
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def get_cost_waterfall(
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db_path: Path,
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date_filter_id: str,
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chart_type: str,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 2 directorate/indication nodes with cost metrics.
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Since level 2 cost_pp_pa is 'N/A', we compute it from child (level 3) nodes:
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sum(cost) / sum(patients) for each directory.
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Returns list of dicts: [{directory, patients, total_cost, cost_pp}]
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Sorted by cost_pp desc.
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"""
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conn = sqlite3.connect(str(db_path))
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conn.row_factory = sqlite3.Row
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try:
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where = ["date_filter_id = ?", "chart_type = ?", "level = 3"]
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params: list = [date_filter_id, chart_type]
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if trust:
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where.append("trust_name = ?")
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params.append(trust)
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query = f"""
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SELECT directory, SUM(value) AS patients, SUM(cost) AS total_cost
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FROM pathway_nodes
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WHERE {' AND '.join(where)}
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GROUP BY directory
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HAVING patients > 0
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ORDER BY total_cost DESC
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"""
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rows = conn.execute(query, params).fetchall()
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result = []
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for r in rows:
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patients = r["patients"] or 0
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total_cost = float(r["total_cost"]) if r["total_cost"] else 0.0
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result.append({
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"directory": r["directory"] or "",
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"patients": patients,
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"total_cost": round(total_cost, 2),
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"cost_pp": round(total_cost / patients, 2) if patients else 0,
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})
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result.sort(key=lambda x: -x["cost_pp"])
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return result
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except sqlite3.Error:
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return []
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finally:
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conn.close()
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def get_drug_transitions(
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db_path: Path,
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date_filter_id: str,
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chart_type: str,
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directory: Optional[str] = None,
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trust: Optional[str] = None,
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) -> dict:
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"""Parse level 3+ nodes into source→target drug transitions for Sankey.
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Returns dict with:
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nodes: [{name, total_patients}] — unique drug names
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links: [{source_idx, target_idx, patients}] — transitions between drugs
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"""
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conn = sqlite3.connect(str(db_path))
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conn.row_factory = sqlite3.Row
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try:
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where = ["date_filter_id = ?", "chart_type = ?", "level >= 4"]
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params: list = [date_filter_id, chart_type]
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if directory:
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where.append("directory = ?")
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params.append(directory)
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if trust:
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where.append("trust_name = ?")
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params.append(trust)
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query = f"""
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SELECT ids, level, value AS patients, drug_sequence, directory
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FROM pathway_nodes
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WHERE {' AND '.join(where)}
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ORDER BY level, ids
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"""
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rows = conn.execute(query, params).fetchall()
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# Build node list and link aggregation
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# Each drug at each treatment line is a separate Sankey node
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# e.g., "ADALIMUMAB (1st)" and "ADALIMUMAB (2nd)" are different nodes
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drug_line_set = set()
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link_agg = {}
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for r in rows:
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drugs = [d for d in (r["drug_sequence"] or "").split("|") if d]
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patients = r["patients"] or 0
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if len(drugs) < 2 or patients == 0:
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continue
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# Only use adjacent transitions (drug[i] → drug[i+1])
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for i in range(len(drugs) - 1):
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src = f"{drugs[i]} ({_ordinal(i + 1)})"
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tgt = f"{drugs[i + 1]} ({_ordinal(i + 2)})"
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drug_line_set.add(src)
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drug_line_set.add(tgt)
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key = (src, tgt)
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link_agg[key] = link_agg.get(key, 0) + patients
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# Build indexed node list
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node_list = sorted(drug_line_set)
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node_idx = {name: i for i, name in enumerate(node_list)}
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nodes = [{"name": name} for name in node_list]
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links = [
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{"source_idx": node_idx[src], "target_idx": node_idx[tgt], "patients": pts}
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for (src, tgt), pts in sorted(link_agg.items(), key=lambda x: -x[1])
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]
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return {"nodes": nodes, "links": links}
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except sqlite3.Error:
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return {"nodes": [], "links": []}
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finally:
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conn.close()
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def _ordinal(n: int) -> str:
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"""Return '1st', '2nd', '3rd', '4th', etc."""
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if 11 <= n % 100 <= 13:
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return f"{n}th"
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suffix = {1: "st", 2: "nd", 3: "rd"}.get(n % 10, "th")
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return f"{n}{suffix}"
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def get_dosing_intervals(
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db_path: Path,
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date_filter_id: str,
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chart_type: str,
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drug: Optional[str] = None,
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trust: Optional[str] = None,
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) -> list[dict]:
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"""Level 3 drug nodes with parsed dosing interval data.
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Returns list of dicts:
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[{drug, trust_name, directory, weekly_interval, dose_count, total_weeks, patients}]
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"""
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from data_processing.parsing import parse_average_spacing
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conn = sqlite3.connect(str(db_path))
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conn.row_factory = sqlite3.Row
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try:
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where = ["date_filter_id = ?", "chart_type = ?", "level = 3",
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"average_spacing IS NOT NULL", "average_spacing != ''"]
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params: list = [date_filter_id, chart_type]
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if drug:
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where.append("labels = ?")
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params.append(drug)
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if trust:
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where.append("trust_name = ?")
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params.append(trust)
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query = f"""
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SELECT labels AS drug, trust_name, directory, value AS patients,
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average_spacing
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FROM pathway_nodes
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WHERE {' AND '.join(where)}
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ORDER BY labels, trust_name
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"""
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rows = conn.execute(query, params).fetchall()
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result = []
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for r in rows:
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parsed = parse_average_spacing(r["average_spacing"])
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for entry in parsed:
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result.append({
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"drug": entry["drug_name"],
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"trust_name": r["trust_name"] or "",
|
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"directory": r["directory"] or "",
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"weekly_interval": entry["weekly_interval"],
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"dose_count": entry["dose_count"],
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"total_weeks": entry["total_weeks"],
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"patients": r["patients"] or 0,
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})
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return result
|
||||
except sqlite3.Error:
|
||||
return []
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def get_drug_directory_matrix(
|
||||
db_path: Path,
|
||||
date_filter_id: str,
|
||||
chart_type: str,
|
||||
trust: Optional[str] = None,
|
||||
) -> dict:
|
||||
"""Level 3 nodes pivoted as directory × drug matrix.
|
||||
|
||||
Returns dict with:
|
||||
directories: sorted list of directory names (rows)
|
||||
drugs: sorted list of drug names (columns)
|
||||
matrix: {directory: {drug: {patients, cost, cost_pp_pa}}}
|
||||
"""
|
||||
conn = sqlite3.connect(str(db_path))
|
||||
conn.row_factory = sqlite3.Row
|
||||
try:
|
||||
where = ["date_filter_id = ?", "chart_type = ?", "level = 3"]
|
||||
params: list = [date_filter_id, chart_type]
|
||||
|
||||
if trust:
|
||||
where.append("trust_name = ?")
|
||||
params.append(trust)
|
||||
|
||||
query = f"""
|
||||
SELECT labels AS drug, directory, value AS patients, cost, cost_pp_pa
|
||||
FROM pathway_nodes
|
||||
WHERE {' AND '.join(where)}
|
||||
ORDER BY directory, labels
|
||||
"""
|
||||
rows = conn.execute(query, params).fetchall()
|
||||
|
||||
# Aggregate across trusts
|
||||
matrix = {}
|
||||
dir_totals = {}
|
||||
drug_totals = {}
|
||||
|
||||
for r in rows:
|
||||
d = r["directory"] or ""
|
||||
drug = r["drug"] or ""
|
||||
patients = r["patients"] or 0
|
||||
cost = float(r["cost"]) if r["cost"] else 0.0
|
||||
cpp = _safe_float(r["cost_pp_pa"])
|
||||
|
||||
if d not in matrix:
|
||||
matrix[d] = {}
|
||||
if drug not in matrix[d]:
|
||||
matrix[d][drug] = {"patients": 0, "cost": 0.0}
|
||||
|
||||
matrix[d][drug]["patients"] += patients
|
||||
matrix[d][drug]["cost"] += cost
|
||||
|
||||
dir_totals[d] = dir_totals.get(d, 0) + patients
|
||||
drug_totals[drug] = drug_totals.get(drug, 0) + patients
|
||||
|
||||
# Add cost_pp_pa to each cell
|
||||
for d in matrix:
|
||||
for drug in matrix[d]:
|
||||
cell = matrix[d][drug]
|
||||
cell["cost"] = round(cell["cost"], 2)
|
||||
cell["cost_pp_pa"] = (
|
||||
round(cell["cost"] / cell["patients"], 2) if cell["patients"] else 0
|
||||
)
|
||||
|
||||
# Sort directories by total patients desc, drugs by frequency desc
|
||||
directories = sorted(dir_totals, key=lambda x: -dir_totals[x])
|
||||
drugs = sorted(drug_totals, key=lambda x: -drug_totals[x])
|
||||
|
||||
return {"directories": directories, "drugs": drugs, "matrix": matrix}
|
||||
except sqlite3.Error:
|
||||
return {"directories": [], "drugs": [], "matrix": {}}
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def get_treatment_durations(
|
||||
db_path: Path,
|
||||
date_filter_id: str,
|
||||
chart_type: str,
|
||||
directory: Optional[str] = None,
|
||||
trust: Optional[str] = None,
|
||||
) -> list[dict]:
|
||||
"""Level 3 drug nodes with average treatment duration.
|
||||
|
||||
Returns list of dicts: [{drug, avg_days, patients, directory}]
|
||||
Sorted by avg_days desc. Excludes nodes with no avg_days data.
|
||||
"""
|
||||
conn = sqlite3.connect(str(db_path))
|
||||
conn.row_factory = sqlite3.Row
|
||||
try:
|
||||
where = ["date_filter_id = ?", "chart_type = ?", "level = 3",
|
||||
"avg_days IS NOT NULL"]
|
||||
params: list = [date_filter_id, chart_type]
|
||||
|
||||
if directory:
|
||||
where.append("directory = ?")
|
||||
params.append(directory)
|
||||
if trust:
|
||||
where.append("trust_name = ?")
|
||||
params.append(trust)
|
||||
|
||||
query = f"""
|
||||
SELECT labels AS drug, directory, trust_name,
|
||||
value AS patients, avg_days
|
||||
FROM pathway_nodes
|
||||
WHERE {' AND '.join(where)}
|
||||
ORDER BY avg_days DESC
|
||||
"""
|
||||
rows = conn.execute(query, params).fetchall()
|
||||
|
||||
# Aggregate across trusts: weighted average of avg_days by patients
|
||||
agg = {}
|
||||
for r in rows:
|
||||
key = (r["directory"] or "", r["drug"])
|
||||
patients = r["patients"] or 0
|
||||
days = _safe_float(r["avg_days"])
|
||||
if patients == 0 or days == 0:
|
||||
continue
|
||||
|
||||
if key not in agg:
|
||||
agg[key] = {"drug": r["drug"], "directory": r["directory"] or "",
|
||||
"total_weighted_days": 0.0, "total_patients": 0}
|
||||
agg[key]["total_weighted_days"] += days * patients
|
||||
agg[key]["total_patients"] += patients
|
||||
|
||||
result = []
|
||||
for v in agg.values():
|
||||
if v["total_patients"] > 0:
|
||||
result.append({
|
||||
"drug": v["drug"],
|
||||
"directory": v["directory"],
|
||||
"avg_days": round(v["total_weighted_days"] / v["total_patients"], 1),
|
||||
"patients": v["total_patients"],
|
||||
})
|
||||
|
||||
result.sort(key=lambda x: -x["avg_days"])
|
||||
return result
|
||||
except sqlite3.Error:
|
||||
return []
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
Reference in New Issue
Block a user