475 create a script to generate random performance data (#476)

* random performance data generator created.

Author: faridseifi

.gitignore

@@ -31,3 +31,4 @@ PFPenv/
 .env.local
 .ruff_cache/
 .pytest_cache/
+bulk-up/random_performance_data/

bulk-up/poetry.lock

@@ -18,6 +18,7 @@ openpyxl = "^3.1.2"
 rdflib = "^7.0.0"
 pyyaml = "^6.0.2"
 loguru = "^0.7.2"
+orjson = "^3.11.0"
 
 
 [tool.poetry.group.dev.dependencies]

bulk-up/pyproject.toml

@@ -0,0 +1,213 @@
+import json
+import random
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+from dateutil.relativedelta import relativedelta
+
+
+def generate_preferences(probability=0.035):
+    if random.random() > probability:
+        return {}
+
+    def random_float(min_val, max_val, decimals=2):
+        return round(random.uniform(min_val, max_val), decimals)
+
+    # Randomly choose one of the display formats to be 1
+    display_options = ["Bar chart", "Line chart", "Text-only", "System-generated"]
+    selected_display = random.choice(display_options)
+    display_format = {
+        option: 1 if option == selected_display else 0 for option in display_options
+    }
+
+    preferences = {
+        "Utilities": {
+            "Message_Format": {
+                "Social gain": str(random_float(0.01, 0.1)),
+                "Social stayed better": str(random_float(-0.2, -0.05)),
+                "Worsening": str(random_float(-0.2, -0.05)),
+                "Improving": str(random_float(-0.2, -0.05)),
+                "Social loss": str(random_float(0.5, 0.8)),
+                "Social stayed worse": str(random_float(-0.7, -0.4)),
+                "Social better": str(random_float(-1.4, -1.0)),
+                "Social worse": str(random_float(0.3, 0.6)),
+                "Social approach": str(random_float(0.8, 1.1)),
+                "Goal gain": str(random_float(0.01, 0.08)),
+                "Goal approach": str(random_float(0.8, 1.1)),
+            },
+            "Display_Format": display_format,
+        }
+    }
+
+    return preferences
+
+
+# Variables
+performance_month = "2025-01-01"
+performance_date = datetime.strptime(performance_month, "%Y-%m-%d")
+measures = [
+    "BP01",
+    "BP02",
+    "BP03",
+    "BP04",
+    "BP05",
+    "BP06",
+    "GLU01",
+    "GLU02",
+    "GLU03",
+    "GLU04",
+    "GLU05",
+    "NMB01",
+    "NMB02",
+    "NMB03",
+    "PAIN01",
+    "PAIN02",
+    "PONV01",
+    "PONV04",
+    "PONV05",
+    "PUL01",
+    "PUL02",
+    "PUL03",
+    "SMOK01",
+    "SMOK02",
+    "SMOK03",
+    "SUS01",
+    "SUS02",
+    "SUS03",
+    "SUS04",
+    "SUS05",
+    "TEMP01",
+    "TEMP02",
+    "TEMP03",
+    "TOC01",
+    "TOC02",
+    "TOC03",
+    "TRAN01",
+]
+institutions = list(range(1, 51))
+num_months = 12
+months = [
+    (performance_date - relativedelta(months=i)).strftime("%Y-%m-%d")
+    for i in reversed(range(0, num_months))
+]
+
+# Output directory
+output_dir = Path("random_performance_data")
+output_dir.mkdir(exist_ok=True)
+
+# generate comparator values
+
+
+# Generate list of 6 months before performance_month
+
+
+# Output dictionary
+comparators = {}
+
+for inst in institutions:
+    comparators[inst] = {}
+    for measure in measures:
+        comparators[inst][measure] = {}
+        for date in months:
+            # Generate values with the correct constraints
+            peer_average = round(random.uniform(55.0, 90.0), 1)
+            peer_90th = round(random.uniform(90.1, 99.9), 1)
+            peer_75th = round(random.uniform(peer_average + 0.1, peer_90th - 0.1), 1)
+
+            # Safety check
+            if peer_75th <= peer_average:
+                peer_75th = round(peer_average + 0.5, 1)
+            if peer_90th <= peer_75th:
+                peer_90th = round(peer_75th + 0.5, 1)
+
+            comparators[inst][measure][date] = {
+                "peer_average_comparator": peer_average,
+                "peer_75th_percentile_benchmark": peer_75th,
+                "peer_90th_percentile_benchmark": peer_90th,
+                "MPOG_goal": 90.0,
+            }
+
+
+# Counter to ensure global uniqueness
+global_staff_counter = 1
+
+for institution in institutions:
+    num_staff = random.randint(5, 25)
+    for staff_number in range(num_staff):
+        staff_data = {
+            "@context": {
+                "@vocab": "http://schema.org/",
+                "slowmo": "http://example.com/slowmo#",
+                "csvw": "http://www.w3.org/ns/csvw#",
+                "dc": "http://purl.org/dc/terms/",
+                "psdo": "http://purl.obolibrary.org/obo/",
+                "slowmo:Measure": "http://example.com/slowmo#Measure",
+                "slowmo:IsAboutPerformer": "http://example.com/slowmo#IsAboutPerformer",
+                "slowmo:ColumnUse": "http://example.com/slowmo#ColumnUse",
+                "slowmo:IsAboutTemplate": "http://example.com/slowmo#IsAboutTemplate",
+                "slowmo:spek": "http://example.com/slowmo#spek",
+                "slowmo:IsAboutCausalPathway": "http://example.com/slowmo#IsAboutCausalPathway",
+                "slowmo:IsAboutOrganization": "http://example.com/slowmo#IsAboutOrganization",
+                "slowmo:IsAboutMeasure": "http://example.com/slowmo#IsAboutMeasure",
+                "slowmo:InputTable": "http://example.com/slowmo#InputTable",
+                "slowmo:WithComparator": "http://example.com/slowmo#WithComparator",
+                "has_part": "http://purl.obolibrary.org/obo/bfo#BFO_0000051",
+                "has_disposition": "http://purl.obolibrary.org/obo/RO_0000091",
+            },
+            "message_instance_id": f"{str(uuid.uuid4())}",
+            "performance_month": performance_month,
+            "staff_number": f"STAFF-{global_staff_counter:06}",  # e.g., STAFF-000001
+            "institution_id": institution,
+            "Performance_data": [
+                [
+                    "staff_number",
+                    "measure",
+                    "month",
+                    "passed_count",
+                    "flagged_count",
+                    "denominator",
+                    "peer_average_comparator",
+                    "peer_75th_percentile_benchmark",
+                    "peer_90th_percentile_benchmark",
+                    "MPOG_goal",
+                ],
+            ],
+            "History": {},
+            "Preferences": generate_preferences(),
+            "debug": "no",
+        }
+
+        for measure in comparators[institution]:
+            for month in comparators[institution][measure]:
+                # Get comparator values
+                comparator_values = comparators[institution][measure][month]
+
+                # Random denominator, passed ≥ 1
+                denominator = random.randint(1, 40)
+                passed_count = random.randint(1, denominator)
+                flagged_count = denominator - passed_count
+
+                # Build row
+                row = [
+                    global_staff_counter,
+                    measure,
+                    month,
+                    passed_count,
+                    flagged_count,
+                    denominator,
+                    comparator_values["peer_average_comparator"],
+                    comparator_values["peer_75th_percentile_benchmark"],
+                    comparator_values["peer_90th_percentile_benchmark"],
+                    comparator_values["MPOG_goal"],
+                ]
+                staff_data["Performance_data"].append(row)
+
+        file_name = f"Provider_{global_staff_counter}.json"
+        file_path = output_dir / file_name
+
+        # Write JSON file
+        with open(file_path, "w") as f:
+            json.dump(staff_data, f, indent=2)
+
+        global_staff_counter += 1