Centering the interval for vl uncertainty computation in report gener…

…ator -> improving test accuracy

caimira/apps/calculator/report_generator.py

@@ -199,7 +199,7 @@ def conditional_prob_inf_given_vl_dist(infection_probability: models._Vectorised
     upper_percentiles = []
 
     for vl_log in viral_loads:
-        specific_prob = infection_probability[np.where((vl_log-specific_vl)*(vl_log+step-specific_vl)<0)[0]] #type: ignore
+        specific_prob = infection_probability[np.where((vl_log-step/2-specific_vl)*(vl_log+step/2-specific_vl)<0)[0]] #type: ignore
         pi_means.append(specific_prob.mean())
         lower_percentiles.append(np.quantile(specific_prob, 0.05))
         upper_percentiles.append(np.quantile(specific_prob, 0.95))

caimira/tests/test_conditional_probability.py

@@ -43,7 +43,7 @@ def baseline_exposure_model():
     )
 
 
-@retry(tries=10)
+@retry(tries=2)
 def test_conditional_prob_inf_given_vl_dist(baseline_exposure_model):
 
     viral_loads = np.array([3., 5., 7., 9.,])
@@ -67,11 +67,11 @@ def test_conditional_prob_inf_given_vl_dist(baseline_exposure_model):
 
     infection_probability = mc_model.infection_probability() / 100    
     specific_vl = np.log10(mc_model.concentration_model.infected.virus.viral_load_in_sputum)
-    step = (max(viral_loads) - min(viral_loads))/100
+    step = 8/100
     actual_pi_means, actual_lower_percentiles, actual_upper_percentiles = (
         report_generator.conditional_prob_inf_given_vl_dist(infection_probability, viral_loads, specific_vl, step)
     )
 
-    assert np.allclose(actual_pi_means, expected_pi_means, rtol=0.1)
-    assert np.allclose(actual_lower_percentiles, expected_lower_percentiles, rtol=0.1)
-    assert np.allclose(actual_upper_percentiles, expected_upper_percentiles, rtol=0.1)
+    assert np.allclose(actual_pi_means, expected_pi_means, atol=0.001)
+    assert np.allclose(actual_lower_percentiles, expected_lower_percentiles, atol=0.001)
+    assert np.allclose(actual_upper_percentiles, expected_upper_percentiles, atol=0.001)