fix: harden admin access, repair ORM joins, and add migration/tests
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dwindown
12
tests/test_model_mappings.py
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12
tests/test_model_mappings.py
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from sqlalchemy.orm import configure_mappers
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def test_sqlalchemy_mappers_configure_without_join_errors():
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"""
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Ensure relationship joins are fully resolvable.
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This catches missing FK/primaryjoin regressions early.
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"""
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import app.models # noqa: F401
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configure_mappers()
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@@ -1,275 +1,77 @@
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#!/usr/bin/env python3
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"""
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Test script for normalization calculations.
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This script tests the normalization functions to ensure they work correctly
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without requiring database connections.
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"""
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import sys
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import math
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import os
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import sys
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# Add the project root to the path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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import pytest
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# Ensure project root is importable when tests run in isolated environments.
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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from app.services.normalization import apply_normalization
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def test_apply_normalization():
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"""Test the apply_normalization function."""
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print("Testing apply_normalization function...")
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print("=" * 60)
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# Test case 1: Normal normalization (NM=500, rataan=500, sb=100)
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nm1 = 500
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rataan1 = 500
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sb1 = 100
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nn1 = apply_normalization(nm1, rataan1, sb1)
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expected1 = 500
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print(f"Test 1: NM={nm1}, rataan={rataan1}, sb={sb1}")
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print(f" Expected NN: {expected1}")
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print(f" Actual NN: {nn1}")
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print(f" Status: {'PASS' if nn1 == expected1 else 'FAIL'}")
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print()
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# Test case 2: High score (NM=600, rataan=500, sb=100)
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nm2 = 600
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rataan2 = 500
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sb2 = 100
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nn2 = apply_normalization(nm2, rataan2, sb2)
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expected2 = 600
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print(f"Test 2: NM={nm2}, rataan={rataan2}, sb={sb2}")
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print(f" Expected NN: {expected2}")
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print(f" Actual NN: {nn2}")
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print(f" Status: {'PASS' if nn2 == expected2 else 'FAIL'}")
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print()
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# Test case 3: Low score (NM=400, rataan=500, sb=100)
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nm3 = 400
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rataan3 = 500
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sb3 = 100
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nn3 = apply_normalization(nm3, rataan3, sb3)
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expected3 = 400
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print(f"Test 3: NM={nm3}, rataan={rataan3}, sb={sb3}")
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print(f" Expected NN: {expected3}")
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print(f" Actual NN: {nn3}")
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print(f" Status: {'PASS' if nn3 == expected3 else 'FAIL'}")
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print()
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# Test case 4: Edge case - maximum NM
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nm4 = 1000
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rataan4 = 500
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sb4 = 100
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nn4 = apply_normalization(nm4, rataan4, sb4)
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expected4 = 1000
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print(f"Test 4: NM={nm4}, rataan={rataan4}, sb={sb4}")
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print(f" Expected NN: {expected4}")
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print(f" Actual NN: {nn4}")
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print(f" Status: {'PASS' if nn4 == expected4 else 'FAIL'}")
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print()
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# Test case 5: Edge case - minimum NM
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nm5 = 0
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rataan5 = 500
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sb5 = 100
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nn5 = apply_normalization(nm5, rataan5, sb5)
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expected5 = 0
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print(f"Test 5: NM={nm5}, rataan={rataan5}, sb={sb5}")
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print(f" Expected NN: {expected5}")
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print(f" Actual NN: {nn5}")
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print(f" Status: {'PASS' if nn5 == expected5 else 'FAIL'}")
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print()
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# Test case 6: Error case - invalid NM (above max)
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try:
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nm6 = 1200 # Above valid range
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rataan6 = 500
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sb6 = 100
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nn6 = apply_normalization(nm6, rataan6, sb6)
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print(f"Test 6: NM={nm6}, rataan={rataan6}, sb={sb6} (should raise ValueError)")
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print(f" Status: FAIL - Should have raised ValueError")
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except ValueError as e:
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print(f"Test 6: NM={nm6}, rataan={rataan6}, sb={sb6} (should raise ValueError)")
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print(f" Error: {e}")
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print(f" Status: PASS - Correctly raised ValueError")
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print()
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# Test case 7: Error case - invalid NM (below min)
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try:
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nm7 = -100 # Below valid range
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rataan7 = 500
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sb7 = 100
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nn7 = apply_normalization(nm7, rataan7, sb7)
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print(f"Test 7: NM={nm7}, rataan={rataan7}, sb={sb7} (should raise ValueError)")
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print(f" Status: FAIL - Should have raised ValueError")
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except ValueError as e:
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print(f"Test 7: NM={nm7}, rataan={rataan7}, sb={sb7} (should raise ValueError)")
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print(f" Error: {e}")
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print(f" Status: PASS - Correctly raised ValueError")
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print()
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# Test case 8: Different rataan/sb (NM=500, rataan=600, sb=80)
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nm8 = 500
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rataan8 = 600
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sb8 = 80
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nn8 = apply_normalization(nm8, rataan8, sb8)
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# z_score = (500 - 600) / 80 = -1.25
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# nn = 500 + 100 * (-1.25) = 500 - 125 = 375
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expected8 = 375
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print(f"Test 8: NM={nm8}, rataan={rataan8}, sb={sb8}")
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print(f" Expected NN: {expected8}")
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print(f" Actual NN: {nn8}")
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print(f" Status: {'PASS' if nn8 == expected8 else 'FAIL'}")
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print()
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# Test case 9: Error case - invalid NM
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try:
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nm9 = 1500 # Above valid range
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rataan9 = 500
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sb9 = 100
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nn9 = apply_normalization(nm9, rataan9, sb9)
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print(f"Test 9: NM={nm9}, rataan={rataan9}, sb={sb9} (should raise ValueError)")
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print(f" Status: FAIL - Should have raised ValueError")
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except ValueError as e:
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print(f"Test 9: NM=1500, rataan=500, sb=100 (should raise ValueError)")
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print(f" Error: {e}")
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print(f" Status: PASS - Correctly raised ValueError")
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print()
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# Test case 10: Error case - invalid sb
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try:
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nm10 = 500
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rataan10 = 500
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sb10 = 0 # Invalid SD
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nn10 = apply_normalization(nm10, rataan10, sb10)
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expected10 = 500 # Should return default when sb <= 0
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print(f"Test 10: NM={nm10}, rataan={rataan10}, sb={sb10} (should return default)")
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print(f" Expected NN: {expected10}")
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print(f" Actual NN: {nn10}")
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print(f" Status: {'PASS' if nn10 == expected10 else 'FAIL'}")
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except Exception as e:
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print(f"Test 10: NM=500, rataan=500, sb=0 (should return default)")
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print(f" Error: {e}")
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print(f" Status: FAIL - Should have returned default value")
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print()
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print("=" * 60)
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print("All tests completed!")
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print("=" * 60)
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@pytest.mark.parametrize(
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("nm", "rataan", "sb", "expected"),
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[
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(500, 500, 100, 500),
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(600, 500, 100, 600),
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(400, 500, 100, 400),
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(1000, 500, 100, 1000),
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(0, 500, 100, 0),
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(500, 600, 80, 375),
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],
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)
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def test_apply_normalization_nominal_cases(nm: int, rataan: float, sb: float, expected: int):
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assert apply_normalization(nm, rataan, sb) == expected
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def calculate_dynamic_mean_and_std(nm_values):
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"""
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Calculate mean and standard deviation from a list of NM values.
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This simulates what update_dynamic_normalization does.
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"""
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n = len(nm_values)
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if n == 0:
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return None, None
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# Calculate mean
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mean = sum(nm_values) / n
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# Calculate variance (population variance)
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if n > 1:
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variance = sum((x - mean) ** 2 for x in nm_values) / n
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std = variance ** 0.5
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else:
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std = 0.0
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return mean, std
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@pytest.mark.parametrize("nm", [-1, 1001, 1500, -100])
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def test_apply_normalization_rejects_invalid_nm(nm: int):
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with pytest.raises(ValueError):
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apply_normalization(nm, 500, 100)
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def test_dynamic_normalization_simulation():
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"""Test dynamic normalization with simulated participant scores."""
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print("\nTesting dynamic normalization simulation...")
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print("=" * 60)
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@pytest.mark.parametrize("sb", [0, -1, -100.0])
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def test_apply_normalization_returns_default_when_sd_non_positive(sb: float):
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assert apply_normalization(500, 500, sb) == 500
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# Simulate 10 participant NM scores
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def test_dynamic_normalization_distribution_behaves_as_expected():
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nm_scores = [450, 480, 500, 520, 550, 480, 510, 490, 530, 470]
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print(f"Simulated NM scores: {nm_scores}")
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print()
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# Calculate mean and SD
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mean, std = calculate_dynamic_mean_and_std(nm_scores)
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print(f"Calculated mean (rataan): {mean:.2f}")
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print(f"Calculated SD (sb): {std:.2f}")
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print()
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mean = sum(nm_scores) / len(nm_scores)
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variance = sum((x - mean) ** 2 for x in nm_scores) / len(nm_scores)
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std = math.sqrt(variance)
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# Normalize each score
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print("Normalized scores:")
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for i, nm in enumerate(nm_scores):
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nn = apply_normalization(nm, mean, std)
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print(f" Participant {i+1}: NM={nm:3d} -> NN={nn:3d}")
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print()
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# Check if normalized distribution is close to mean=500, SD=100
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nn_scores = [apply_normalization(nm, mean, std) for nm in nm_scores]
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nn_mean, nn_std = calculate_dynamic_mean_and_std(nn_scores)
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nn_mean = sum(nn_scores) / len(nn_scores)
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nn_variance = sum((x - nn_mean) ** 2 for x in nn_scores) / len(nn_scores)
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nn_std = math.sqrt(nn_variance)
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print(f"Normalized distribution:")
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print(f" Mean: {nn_mean:.2f} (target: 500 ± 5)")
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print(f" SD: {nn_std:.2f} (target: 100 ± 5)")
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print(f" Status: {'PASS' if abs(nn_mean - 500) <= 5 and abs(nn_std - 100) <= 5 else 'NEAR PASS'}")
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print()
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print("=" * 60)
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# Rounding in apply_normalization introduces small drift; these bounds are tight.
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assert abs(nn_mean - 500) <= 5
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assert abs(nn_std - 100) <= 5
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def test_incremental_update():
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"""Test incremental update of dynamic normalization."""
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print("\nTesting incremental update simulation...")
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print("=" * 60)
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def test_incremental_population_stats_match_batch_stats():
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scores = [500, 550, 450, 600, 400]
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# Simulate adding scores incrementally
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nm_scores = []
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participant_count = 0
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total_nm_sum = 0.0
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total_nm_sq_sum = 0.0
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new_scores = [500, 550, 450, 600, 400]
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for i, nm in enumerate(new_scores):
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# Update running statistics
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for score in scores:
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participant_count += 1
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total_nm_sum += nm
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total_nm_sq_sum += nm * nm
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total_nm_sum += score
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total_nm_sq_sum += score * score
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# Calculate mean and SD
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mean = total_nm_sum / participant_count
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if participant_count > 1:
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variance = (total_nm_sq_sum / participant_count) - (mean ** 2)
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std = variance ** 0.5
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else:
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std = 0.0
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incremental_mean = total_nm_sum / participant_count
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incremental_variance = (total_nm_sq_sum / participant_count) - (incremental_mean**2)
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incremental_std = math.sqrt(max(0.0, incremental_variance))
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nm_scores.append(nm)
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batch_mean = sum(scores) / len(scores)
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batch_variance = sum((x - batch_mean) ** 2 for x in scores) / len(scores)
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batch_std = math.sqrt(batch_variance)
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print(f"After adding participant {i+1}:")
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print(f" NM: {nm}")
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print(f" Participant count: {participant_count}")
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print(f" Mean (rataan): {mean:.2f}")
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print(f" SD (sb): {std:.2f}")
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print()
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# Final calculation
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final_mean, final_std = calculate_dynamic_mean_and_std(nm_scores)
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print(f"Final statistics:")
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print(f" All scores: {nm_scores}")
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print(f" Mean: {final_mean:.2f}")
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print(f" SD: {final_std:.2f}")
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print()
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print("=" * 60)
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if __name__ == "__main__":
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print("Normalization Calculation Tests")
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print("=" * 60)
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print()
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test_apply_normalization()
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test_dynamic_normalization_simulation()
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test_incremental_update()
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print("\nAll test simulations completed successfully!")
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assert incremental_mean == pytest.approx(batch_mean, rel=0, abs=1e-10)
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assert incremental_std == pytest.approx(batch_std, rel=0, abs=1e-10)
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