Andrew Charlwood
7e63e6ea45
- resource_path.py: frozen/dev path resolution for bundled data files - app_desktop.py: pywebview entry point (Dash in daemon thread) - app.spec: PyInstaller onedir config with data files and hidden imports - Updated queries.py, card_browser.py, app.py to use get_resource_path() - Added pywebview + pyinstaller to project dependencies - Fixed unresolved merge conflict in .gitignore - Removed stale 01_nhs_classic.html and AdditionalAnalytics.md
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
"""
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Directorate card tree builder for the drug browser drawer.
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Loads DimSearchTerm.csv and builds a nested structure:
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{PrimaryDirectorate: {Search_Term: [drug_fragment, ...]}}
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Also provides get_all_drugs() for the flat "All Drugs" card.
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"""
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import csv
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from collections import defaultdict
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from core.resource_path import get_resource_path
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from data_processing.diagnosis_lookup import SEARCH_TERM_MERGE_MAP
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DIM_SEARCH_TERM_PATH = get_resource_path("data/DimSearchTerm.csv")
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def build_directorate_tree() -> dict[str, dict[str, list[str]]]:
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"""
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Build a nested dict from DimSearchTerm.csv grouped by directorate.
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Returns:
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{
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"CARDIOLOGY": {
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"acute coronary syndrome": ["ABCIXIMAB", "CLOPIDOGREL", ...],
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"atrial fibrillation": ["APIXABAN", "DABIGATRAN", ...],
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...
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},
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"CLINICAL HAEMATOLOGY": { ... },
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...
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}
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Search_Term values are normalized via SEARCH_TERM_MERGE_MAP
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(e.g. "allergic asthma" → "asthma"). Drug fragments within
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merged terms are combined and deduplicated.
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"""
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# directorate → search_term → set of drug fragments
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tree: dict[str, dict[str, set[str]]] = defaultdict(lambda: defaultdict(set))
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with open(DIM_SEARCH_TERM_PATH, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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search_term = (row.get("Search_Term") or "").strip().lower()
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drug_names_raw = row.get("CleanedDrugName") or ""
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directorate = (row.get("PrimaryDirectorate") or "").strip().upper()
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if not search_term or not directorate:
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continue
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# Apply merge map (e.g. "allergic asthma" → "asthma")
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search_term = SEARCH_TERM_MERGE_MAP.get(search_term, search_term)
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fragments = [
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frag.strip().upper()
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for frag in drug_names_raw.split("|")
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if frag.strip()
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]
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tree[directorate][search_term].update(fragments)
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# Convert sets → sorted lists and sort at every level
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result: dict[str, dict[str, list[str]]] = {}
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for directorate in sorted(tree):
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result[directorate] = {
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term: sorted(tree[directorate][term])
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for term in sorted(tree[directorate])
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}
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return result
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def get_all_drugs() -> list[str]:
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"""
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Return a sorted flat list of all unique drug labels from pathway_nodes level 3.
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Delegates to load_initial_data() which already queries the database.
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"""
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from dash_app.data.queries import load_initial_data
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data = load_initial_data()
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return data.get("available_drugs", [])
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def get_all_trusts() -> list[str]:
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"""
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Return a sorted flat list of all unique trust names from pathway_nodes level 1.
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Delegates to load_initial_data() which already queries the database.
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"""
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from dash_app.data.queries import load_initial_data
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data = load_initial_data()
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return data.get("available_trusts", [])
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