feat: add parsing utilities and 8-tab chart infrastructure (Task 9.1)

- Create src/data_processing/parsing.py with parse_average_spacing(),
  parse_pathway_drugs(), and calculate_retention_rate()
- Add 8-tab bar to chart_card.py (Icicle, Market Share, Cost Effectiveness,
  Cost Waterfall, Sankey, Dosing, Heatmap, Duration)
- Add active-tab dcc.Store and tab switching callback in chart.py
- Remove Chart Views section from sidebar (now in tab bar)
- Lazy rendering: only active tab's chart is computed

src/data_processing/parsing.py

@@ -0,0 +1,117 @@
+"""Parsing utilities for pathway node data.
+
+Shared functions for extracting structured data from pathway_nodes columns.
+Used by analytics chart callbacks in dash_app/callbacks/.
+"""
+import re
+
+
+def parse_average_spacing(spacing_html):
+    """Extract dosing information from average_spacing HTML string.
+
+    Args:
+        spacing_html: HTML like '<br><b>DRUG</b><br>On average given 35.6 times
+                      with a 9.0 weekly interval (320.0 weeks total treatment length)'
+                      May contain multiple drug entries separated by <br><b>.
+
+    Returns:
+        List of dicts with keys: drug_name, dose_count, weekly_interval, total_weeks.
+        Returns empty list for None/empty input or unparseable strings.
+    """
+    if not spacing_html:
+        return []
+
+    results = []
+    pattern = (
+        r"<b>([^<]+)</b><br>"
+        r"On average given ([\d.]+) times "
+        r"with a ([\d.]+) weekly interval "
+        r"\(([\d.]+) weeks total treatment length\)"
+    )
+
+    for match in re.finditer(pattern, spacing_html):
+        results.append({
+            "drug_name": match.group(1).strip(),
+            "dose_count": float(match.group(2)),
+            "weekly_interval": float(match.group(3)),
+            "total_weeks": float(match.group(4)),
+        })
+
+    return results
+
+
+def parse_pathway_drugs(ids, level):
+    """Extract ordered drug list from the ids column at level 4+.
+
+    Args:
+        ids: String like 'ROOT - TRUST - DIR - DRUG_A - DRUG_B - DRUG_C'.
+             Segments are separated by ' - '. Drug names start at index 3
+             (0=root, 1=trust, 2=directory, 3+=drugs).
+        level: Node level. Only meaningful for level >= 3.
+
+    Returns:
+        List of drug names in treatment order. Empty list for level < 3
+        or invalid input.
+    """
+    if not ids or level < 3:
+        return []
+
+    segments = ids.split(" - ")
+    # Segments: [root, trust, directory, drug_0, drug_1, ...]
+    if len(segments) <= 3:
+        return []
+
+    return segments[3:]
+
+
+def calculate_retention_rate(nodes):
+    """Calculate pathway retention rates from node data.
+
+    For each N-drug pathway, calculate what % of patients do NOT escalate
+    to an N+1 drug pathway. This identifies effective treatment sequences.
+
+    Args:
+        nodes: List of dicts with 'ids', 'level', 'value' keys.
+               Should contain level 3+ nodes from a single directorate.
+
+    Returns:
+        Dict mapping pathway ids to retention info:
+        {ids: {"retained_patients": int, "total_patients": int,
+               "retention_rate": float, "drug_sequence": list}}
+    """
+    if not nodes:
+        return {}
+
+    # Index nodes by ids for parent lookup
+    node_map = {n["ids"]: n for n in nodes if n.get("ids")}
+
+    results = {}
+    for node in nodes:
+        level = node.get("level", 0)
+        if level < 4:
+            continue
+
+        node_ids = node.get("ids", "")
+        total_patients = node.get("value", 0)
+        if not total_patients:
+            continue
+
+        # Find child pathways (nodes whose ids start with this node's ids + " - ")
+        child_prefix = node_ids + " - "
+        child_patients = sum(
+            n.get("value", 0)
+            for n in nodes
+            if n.get("ids", "").startswith(child_prefix) and n.get("level", 0) == level + 1
+        )
+
+        retained = total_patients - child_patients
+        retention_rate = (retained / total_patients * 100) if total_patients > 0 else 0.0
+
+        results[node_ids] = {
+            "retained_patients": retained,
+            "total_patients": total_patients,
+            "retention_rate": round(retention_rate, 1),
+            "drug_sequence": parse_pathway_drugs(node_ids, level),
+        }
+
+    return results