🔐 Collate random seed setting

bluemira/base/constants.py

@@ -8,6 +8,7 @@
 A collection of generic physical constants, conversions, and miscellaneous constants.
 """
 
+from enum import Enum
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -616,3 +617,15 @@ def gcm3_to_kgm3(
     "lightcyan": "\x1b[96m",
     "darkred": "\x1b[38;5;124m",
 }
+
+
+class RNGSeeds(Enum):
+    """
+    Random Seeds for necessary use cases
+    """
+
+    equilibria_harmonics = 2944412338698111642
+    timeline_tools_lognorm = 6613659347120864846
+    timeline_tools_truncnorm = 9523110846560405221
+    timeline_tools_expo = 15335509124046896388
+    timeline_outages = 5876826953682921855

bluemira/fuel_cycle/timeline.py

@@ -13,7 +13,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from bluemira.base.constants import S_TO_YR, YR_TO_S
+from bluemira.base.constants import S_TO_YR, YR_TO_S, RNGSeeds
 from bluemira.fuel_cycle.timeline_tools import (
     LogNormalAvailabilityStrategy,
     OperationalAvailabilityStrategy,
@@ -175,7 +175,7 @@ def calculate_outages(
 
         dist += self.t_min_down
         self._dist = dist  # Store for plotting/debugging
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(RNGSeeds.timeline_outages.value)
         return rng.permutation(dist)
 
     def plot_dist(self):

bluemira/fuel_cycle/timeline_tools.py

@@ -14,6 +14,7 @@
 import numpy as np
 from scipy.optimize import brentq
 
+from bluemira.base.constants import RNGSeeds
 from bluemira.base.look_and_feel import bluemira_warn
 from bluemira.fuel_cycle.error import FuelCycleError
 
@@ -70,7 +71,7 @@ def generate_lognorm_distribution(n: int, integral: float, sigma: float) -> np.n
     -------
     The distribution of size n and of the correct integral value
     """
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(RNGSeeds.timeline_tools_lognorm.value)
 
     def f_integral(x):
         return np.sum(rng.lognormal(x, sigma, n)) - integral
@@ -100,7 +101,7 @@ def generate_truncnorm_distribution(n: int, integral: float, sigma: float) -> np
     -------
     The distribution of size n and of the correct integral value
     """
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(RNGSeeds.timeline_tools_truncnorm.value)
     distribution = rng.normal(0, sigma, n)
     # Truncate distribution by 0-folding
     distribution = np.abs(distribution)
@@ -129,7 +130,7 @@ def generate_exponential_distribution(
     -------
     The distribution of size n and of the correct integral value
     """
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(RNGSeeds.timeline_tools_expo.value)
     distribution = rng.exponential(lambdda, n)
     # Correct distribution integral
     distribution /= np.sum(distribution)