refactor: slim pathways.db from 351 MB to 3.5 MB by removing unused tables
Drop fact_interventions (440K rows), mv_patient_treatment_summary (35K rows), ref_drug_snomed_mapping (144K rows), and processed_files — all unused since the app moved to pre-computed pathway_nodes. Key changes: - Rewrite load_data() to source from pathway_nodes + pathway_refresh_log - Remove 7 dead methods and 8 dead state vars from pathways_app.py - Delete patient_data.py, load_snomed_mapping.py, test_large_dataset_performance.py - Remove SQLiteDataLoader (depended on fact_interventions) - Remove file tracking schema (processed_files tracked fact_interventions loads) - Remove legacy diagnosis functions from diagnosis_lookup.py - Add source_row_count migration for pathway_refresh_log - Clean all cross-references in __init__.py, data_source.py, migrate.py
This commit is contained in:
Andrew Charlwood
+34
-521
@@ -5,7 +5,7 @@ Single-page dashboard with reactive filtering and real-time chart updates.
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Design reference: DESIGN_SYSTEM.md
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"""
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from datetime import datetime, timedelta
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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@@ -69,14 +69,6 @@ class AppState(rx.State):
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# Data freshness tracking
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last_updated: str = "" # ISO format timestamp of last data load
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# Raw data storage - list of dicts (Reflex-friendly)
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# Each dict represents a patient record with keys like:
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# UPID, Drug Name, Intervention Date, Price Actual, Directory, etc.
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raw_data: list[dict[str, Any]] = []
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# Latest date in dataset (detected on load, used for "to" date defaults)
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latest_date_in_data: str = ""
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# =========================================================================
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# UI State Variables
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# =========================================================================
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@@ -111,22 +103,7 @@ class AppState(rx.State):
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{"value": "12mo", "label": "Last 12 months"},
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]
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# Legacy date filter state (kept for backwards compatibility, will be removed)
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# Filter toggle state
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initiated_filter_enabled: bool = False
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last_seen_filter_enabled: bool = True
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# Date filter values (ISO format strings YYYY-MM-DD)
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# Initiated filter: Defaults empty (filter is OFF by default)
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initiated_from_date: str = ""
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initiated_to_date: str = ""
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# Last Seen filter: Defaults to last 6 months (filter is ON by default)
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# These will be updated on data load to use actual latest date
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last_seen_from_date: str = (datetime.now() - timedelta(days=180)).strftime("%Y-%m-%d")
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last_seen_to_date: str = datetime.now().strftime("%Y-%m-%d")
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# Available options for dropdowns (populated from data in Phase 3)
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# Available options for dropdowns (populated from data)
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available_drugs: list[str] = ["Drug A", "Drug B", "Drug C", "Drug D", "Drug E"]
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available_indications: list[str] = ["Indication 1", "Indication 2", "Indication 3"]
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available_directorates: list[str] = ["Medical", "Surgical", "Oncology", "Rheumatology"]
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@@ -146,45 +123,7 @@ class AppState(rx.State):
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indication_dropdown_open: bool = False
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directorate_dropdown_open: bool = False
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# Event handlers for filter toggles
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def toggle_initiated_filter(self):
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"""Toggle initiated date filter on/off."""
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self.initiated_filter_enabled = not self.initiated_filter_enabled
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if self.data_loaded:
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self.apply_filters()
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def toggle_last_seen_filter(self):
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"""Toggle last seen date filter on/off."""
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self.last_seen_filter_enabled = not self.last_seen_filter_enabled
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if self.data_loaded:
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self.apply_filters()
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# Event handlers for date changes
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def set_initiated_from(self, value: str):
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"""Set initiated from date."""
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self.initiated_from_date = value
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if self.data_loaded:
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self.apply_filters()
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def set_initiated_to(self, value: str):
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"""Set initiated to date."""
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self.initiated_to_date = value
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if self.data_loaded:
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self.apply_filters()
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def set_last_seen_from(self, value: str):
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"""Set last seen from date."""
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self.last_seen_from_date = value
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if self.data_loaded:
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self.apply_filters()
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def set_last_seen_to(self, value: str):
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"""Set last seen to date."""
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self.last_seen_to_date = value
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if self.data_loaded:
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self.apply_filters()
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# Event handlers for date filter dropdowns (new pathway_nodes approach)
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# Event handlers for date filter dropdowns
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def set_initiated_filter(self, value: str):
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"""Set initiated filter dropdown value."""
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self.selected_initiated = value
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@@ -461,141 +400,6 @@ class AppState(rx.State):
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# Filter Logic Methods
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# =========================================================================
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def apply_filters(self):
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"""
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Apply current filter state to data and update KPI values.
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This method queries the SQLite database with the current filter settings:
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- Initiated date filter: filters patients whose FIRST intervention date is within range
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- Last Seen date filter: filters patients whose LAST intervention date is within range
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- Drug filter: filters by selected drugs (empty = all)
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- Directorate filter: filters by selected directorates (empty = all)
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Note: Indication filter is not implemented at the database level since indications
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are derived from drug mappings, not stored directly in fact_interventions.
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Updates: unique_patients, total_drugs, total_cost, and filtered_record_count
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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 = "Unable to connect to database. Please ensure data has been loaded."
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return
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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 the filter query dynamically
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# We use a CTE to compute first_seen and last_seen dates per patient,
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# then filter based on those dates if date filters are enabled
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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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# Build WHERE clause for base data filtering
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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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# Date filter logic:
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# - "Initiated" filters patients whose FIRST intervention is within the date range
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# - "Last Seen" filters patients whose LAST intervention is within the date range
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# We need to use a subquery to compute patient-level date ranges
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having_clauses = []
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having_params = []
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# Initiated filter (when enabled)
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if self.initiated_filter_enabled and self.initiated_from_date:
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having_clauses.append("first_seen_date >= ?")
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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_date <= ?")
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having_params.append(self.initiated_to_date)
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# Last Seen filter (when enabled)
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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_date >= ?")
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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_date <= ?")
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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 filtered patient UPIDs
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# This computes per-patient first/last seen dates and filters accordingly
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patient_filter_query = f"""
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WITH patient_dates AS (
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SELECT
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upid,
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MIN(intervention_date) as first_seen_date,
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MAX(intervention_date) as last_seen_date
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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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SELECT upid FROM patient_dates
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"""
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# Now get KPI values for filtered patients
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kpi_query = f"""
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WITH filtered_patients AS (
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{patient_filter_query}
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)
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SELECT
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COUNT(DISTINCT f.upid) as unique_patients,
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COUNT(DISTINCT f.drug_name_std) as unique_drugs,
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COALESCE(SUM(f.price_actual), 0) as total_cost,
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COUNT(*) as record_count
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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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# Combine all params: base params for CTE, having params, then base params again for final join
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all_params = params + having_params
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if where_clauses:
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all_params.extend(params) # For the AND conditions in the final query
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cursor.execute(kpi_query, all_params)
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result = cursor.fetchone()
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if result:
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self.unique_patients = result[0] or 0
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self.total_drugs = result[1] or 0
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self.total_cost = float(result[2]) if result[2] else 0.0
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# Note: filtered_record_count could be stored if needed
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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"Unable to filter data. Database error: {str(e)}"
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except Exception as e:
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self.error_message = f"An unexpected error occurred while filtering. Details: {str(e)}"
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# =========================================================================
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# Data Loading Methods
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# =========================================================================
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@@ -604,11 +408,8 @@ class AppState(rx.State):
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"""
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Load data from SQLite database on app initialization.
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This method:
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1. Connects to the SQLite database (data/pathways.db)
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2. Loads available drugs, indications, directorates from actual data
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3. Detects the latest date in the dataset for "to" date defaults
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4. Updates total_records, last_updated, and data_loaded state
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Sources available drugs/directorates from pathway_nodes and total_records
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from the latest pathway_refresh_log entry.
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"""
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import sqlite3
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@@ -622,30 +423,38 @@ class AppState(rx.State):
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conn = sqlite3.connect(str(db_path))
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cursor = conn.cursor()
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# Get total records
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cursor.execute("SELECT COUNT(*) FROM fact_interventions")
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self.total_records = cursor.fetchone()[0]
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if self.total_records == 0:
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self.error_message = "The database is empty. No patient records found."
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conn.close()
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return
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# Get available drugs (distinct, sorted)
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# Get total source records from latest completed refresh log
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cursor.execute("""
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SELECT DISTINCT drug_name_std
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FROM fact_interventions
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WHERE drug_name_std IS NOT NULL AND drug_name_std != ''
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ORDER BY drug_name_std
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SELECT source_row_count, completed_at
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FROM pathway_refresh_log
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WHERE status = 'completed'
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ORDER BY started_at DESC
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LIMIT 1
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""")
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refresh_row = cursor.fetchone()
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if refresh_row:
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self.total_records = refresh_row[0] or 0
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if refresh_row[1]:
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self.last_updated = refresh_row[1]
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else:
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self.total_records = 0
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# Get available drugs from pathway_nodes (level 3 = drug nodes)
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cursor.execute("""
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SELECT DISTINCT labels
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FROM pathway_nodes
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WHERE level = 3 AND labels IS NOT NULL AND labels != ''
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ORDER BY labels
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""")
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self.available_drugs = [row[0] for row in cursor.fetchall()]
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# Get available directories (distinct, sorted)
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# Get available directorates from directory chart pathway_nodes (level 2)
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cursor.execute("""
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SELECT DISTINCT directory
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FROM fact_interventions
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WHERE directory IS NOT NULL AND directory != ''
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ORDER BY directory
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SELECT DISTINCT labels
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FROM pathway_nodes
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WHERE level = 2 AND chart_type = 'directory'
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AND labels IS NOT NULL AND labels != ''
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ORDER BY labels
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""")
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self.available_directorates = [row[0] for row in cursor.fetchall()]
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@@ -658,50 +467,17 @@ class AppState(rx.State):
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""")
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self.available_indications = [row[0] for row in cursor.fetchall()]
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# If no indications in reference table, use placeholder
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if not self.available_indications:
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self.available_indications = ["(No indications available)"]
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# Get date range from data
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cursor.execute("""
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SELECT MIN(intervention_date), MAX(intervention_date)
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FROM fact_interventions
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""")
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date_range = cursor.fetchone()
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min_date, max_date = date_range
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# Update latest_date_in_data and set "to" date defaults
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if max_date:
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self.latest_date_in_data = max_date
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self.last_seen_to_date = max_date
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self.initiated_to_date = max_date
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# Set "from" date for last_seen filter (6 months before max_date)
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max_dt = datetime.strptime(max_date, "%Y-%m-%d")
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six_months_ago = max_dt - timedelta(days=180)
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self.last_seen_from_date = six_months_ago.strftime("%Y-%m-%d")
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# Get unique patient count for KPIs
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cursor.execute("SELECT COUNT(DISTINCT upid) FROM fact_interventions")
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self.unique_patients = cursor.fetchone()[0]
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# Get unique drug count
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self.total_drugs = len(self.available_drugs)
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# Get total cost
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cursor.execute("SELECT SUM(price_actual) FROM fact_interventions")
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total_cost_result = cursor.fetchone()[0]
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self.total_cost = float(total_cost_result) if total_cost_result else 0.0
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conn.close()
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# Set data_loaded and last_updated
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self.data_loaded = True
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self.last_updated = datetime.now().isoformat()
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if not self.last_updated:
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self.last_updated = datetime.now().isoformat()
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self.error_message = ""
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# Load pre-computed pathway data for the default date filter
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# This replaces apply_filters() which used dynamic calculation
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self.load_pathway_data()
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except sqlite3.Error as e:
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@@ -986,269 +762,6 @@ class AppState(rx.State):
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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 = "Unable to generate chart. 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,
|
||||
f.drug_name_std,
|
||||
f.price_actual
|
||||
FROM fact_interventions f
|
||||
INNER JOIN filtered_patients fp ON f.upid = fp.upid
|
||||
{base_where.replace('WHERE', 'AND') if base_where else ''}
|
||||
)
|
||||
SELECT
|
||||
trust_name,
|
||||
directory,
|
||||
drug_name_std,
|
||||
COUNT(DISTINCT upid) as patient_count,
|
||||
COALESCE(SUM(price_actual), 0) as total_cost
|
||||
FROM patient_records
|
||||
GROUP BY trust_name, directory, drug_name_std
|
||||
ORDER BY trust_name, directory, drug_name_std
|
||||
"""
|
||||
|
||||
all_params = params + having_params
|
||||
if where_clauses:
|
||||
all_params.extend(params)
|
||||
|
||||
cursor.execute(chart_query, all_params)
|
||||
rows = cursor.fetchall()
|
||||
|
||||
conn.close()
|
||||
|
||||
# Build hierarchical chart data
|
||||
chart_data = []
|
||||
hierarchy_totals = {} # Track totals for calculating color values
|
||||
|
||||
# Root node
|
||||
root_id = "N&WICS"
|
||||
chart_data.append({
|
||||
"parents": "",
|
||||
"ids": root_id,
|
||||
"labels": "Norfolk & Waveney ICS",
|
||||
"value": 0,
|
||||
"cost": 0.0,
|
||||
"colour": 1.0,
|
||||
})
|
||||
|
||||
# Process rows to build hierarchy
|
||||
trust_totals = {}
|
||||
directory_totals = {}
|
||||
drug_data = []
|
||||
|
||||
for row in rows:
|
||||
trust_name, directory, drug_name, patient_count, cost = row
|
||||
|
||||
if not trust_name or not directory or not drug_name:
|
||||
continue
|
||||
|
||||
# Trust level
|
||||
trust_id = f"{root_id} - {trust_name}"
|
||||
if trust_id not in trust_totals:
|
||||
trust_totals[trust_id] = {"value": 0, "cost": 0.0, "label": trust_name}
|
||||
trust_totals[trust_id]["value"] += patient_count
|
||||
trust_totals[trust_id]["cost"] += cost
|
||||
|
||||
# Directory level
|
||||
dir_id = f"{trust_id} - {directory}"
|
||||
if dir_id not in directory_totals:
|
||||
directory_totals[dir_id] = {
|
||||
"value": 0,
|
||||
"cost": 0.0,
|
||||
"label": directory,
|
||||
"parent": trust_id,
|
||||
}
|
||||
directory_totals[dir_id]["value"] += patient_count
|
||||
directory_totals[dir_id]["cost"] += cost
|
||||
|
||||
# Drug level (leaf)
|
||||
drug_id = f"{dir_id} - {drug_name}"
|
||||
drug_data.append({
|
||||
"ids": drug_id,
|
||||
"labels": drug_name,
|
||||
"parent": dir_id,
|
||||
"value": patient_count,
|
||||
"cost": float(cost),
|
||||
})
|
||||
|
||||
# Calculate root total
|
||||
root_total = sum(t["value"] for t in trust_totals.values())
|
||||
root_cost = sum(t["cost"] for t in trust_totals.values())
|
||||
chart_data[0]["value"] = root_total
|
||||
chart_data[0]["cost"] = root_cost
|
||||
|
||||
# Add trust nodes with color proportions
|
||||
for trust_id, data in trust_totals.items():
|
||||
colour = data["value"] / root_total if root_total > 0 else 0
|
||||
chart_data.append({
|
||||
"parents": root_id,
|
||||
"ids": trust_id,
|
||||
"labels": data["label"],
|
||||
"value": data["value"],
|
||||
"cost": data["cost"],
|
||||
"colour": colour,
|
||||
})
|
||||
|
||||
# Add directory nodes with color proportions
|
||||
for dir_id, data in directory_totals.items():
|
||||
parent_total = trust_totals[data["parent"]]["value"]
|
||||
colour = data["value"] / parent_total if parent_total > 0 else 0
|
||||
chart_data.append({
|
||||
"parents": data["parent"],
|
||||
"ids": dir_id,
|
||||
"labels": data["label"],
|
||||
"value": data["value"],
|
||||
"cost": data["cost"],
|
||||
"colour": colour,
|
||||
})
|
||||
|
||||
# Add drug nodes with color proportions
|
||||
for drug in drug_data:
|
||||
parent_dir = drug["parent"]
|
||||
parent_total = directory_totals[parent_dir]["value"]
|
||||
colour = drug["value"] / parent_total if parent_total > 0 else 0
|
||||
chart_data.append({
|
||||
"parents": parent_dir,
|
||||
"ids": drug["ids"],
|
||||
"labels": drug["labels"],
|
||||
"value": drug["value"],
|
||||
"cost": drug["cost"],
|
||||
"colour": colour,
|
||||
})
|
||||
|
||||
self.chart_data = chart_data
|
||||
self.chart_title = self._generate_chart_title()
|
||||
self.chart_loading = False
|
||||
self.error_message = ""
|
||||
|
||||
except sqlite3.Error as e:
|
||||
self.error_message = f"Unable to generate chart. Database error: {str(e)}"
|
||||
self.chart_data = []
|
||||
self.chart_loading = False
|
||||
except Exception as e:
|
||||
self.error_message = f"Unable to generate chart. Details: {str(e)}"
|
||||
self.chart_data = []
|
||||
self.chart_loading = False
|
||||
|
||||
def _generate_chart_title(self) -> str:
|
||||
"""Generate chart title based on current filter state."""
|
||||
parts = []
|
||||
|
||||
# Date range info
|
||||
if self.last_seen_filter_enabled:
|
||||
parts.append(f"Last seen: {self.last_seen_from_date} to {self.last_seen_to_date}")
|
||||
elif self.initiated_filter_enabled:
|
||||
parts.append(f"Initiated: {self.initiated_from_date} to {self.initiated_to_date}")
|
||||
|
||||
# Drug selection info
|
||||
if self.selected_drugs:
|
||||
if len(self.selected_drugs) <= 3:
|
||||
parts.append(", ".join(self.selected_drugs))
|
||||
else:
|
||||
parts.append(f"{len(self.selected_drugs)} drugs selected")
|
||||
|
||||
# Directorate selection info
|
||||
if self.selected_directorates:
|
||||
if len(self.selected_directorates) <= 2:
|
||||
parts.append(", ".join(self.selected_directorates))
|
||||
else:
|
||||
parts.append(f"{len(self.selected_directorates)} directorates")
|
||||
|
||||
if parts:
|
||||
return " | ".join(parts)
|
||||
return "All Patients"
|
||||
|
||||
# =========================================================================
|
||||
# Plotly Chart Generation
|
||||
# =========================================================================
|
||||
|
||||
Reference in New Issue
Block a user