removing random seed

Author: crangelsmith

person_story.py

@@ -236,13 +236,12 @@ def generate_paired_measurement(
         index_gender = 1
 
     sample_epsilon = np.random.normal(src_stats[relative_change_key][index_gender][key_epsilon_mean],
-                                      src_stats[relative_change_key][index_gender][key_epsilon_std], 1)
+                                      src_stats[relative_change_key][index_gender][key_epsilon_std], 1)[0]
 
     systolic_value = np.round(generate_time_series(len(event_datetimes), 'random_walk',
                                                    {'mean': src_stats[main_key][index_gender][key_mean],
-                                                    'std': src_stats[main_key][0][key_std],
-                                                    'epsilon_std': sample_epsilon, 'drift': 0},
-                                                   random_state=42))
+                                                    'std': src_stats[main_key][index_gender][key_std],
+                                                    'epsilon_std': sample_epsilon, 'drift': 0}))
 
     # diastolic value is calculated based on systolic value plus the average difference extrated from data
     # we add some variation to the difference between systolic and diastolic
@@ -296,7 +295,7 @@ def generate(
         src_stats["avg_measurements_per_visit_hour"][0]['stddev_measurements_per_hour'])
     )
 
-    print(f"Generating blood pressure events at an average rate of {avg_rate} per hour.")
+    print(f"Generating blood pressure events at an average rate of {avg_rate} per hour. Using IID sampling.")
     for event in gen_blood_pressure_events(
             avg_rate,
             visit_occurrence,

sqlsynthgen/utils.py

@@ -185,7 +185,6 @@ def generate_time_series(
     N: int,
     model_option: Literal["iid", "random_walk", "ar1"],
     model_params: Dict[str, Any],
-    random_state: int  = 42
 ) -> np.ndarray:
     """
     Generate a synthetic time series using one of three simple models.
@@ -221,13 +220,7 @@ def generate_time_series(
         Synthetic time series of length N.
     """
 
-    rng = np.random.default_rng(random_state)
-
-    # Initialise x0 from marginal distribution
-    x0: float = rng.normal(
-        loc=model_params["mean"],
-        scale=model_params["std"]
-    )
+    rng = np.random.default_rng(None)
 
     # ----------------------------
     # MODEL 1: IID Gaussian
@@ -243,6 +236,11 @@ def generate_time_series(
     # ----------------------------
     # MODEL 2: Random Walk
     # ----------------------------
+
+    x0: float = rng.normal(
+        loc=model_params["mean"],
+        scale=model_params["std"]
+    )
     if model_option == "random_walk":
         required = ["drift", "epsilon_std"]
         for key in required: