diff --git a/TopasOpt/Optimisers.py b/TopasOpt/Optimisers.py
index 21623f4..713403b 100755
--- a/TopasOpt/Optimisers.py
+++ b/TopasOpt/Optimisers.py
@@ -3,27 +3,30 @@
 This module contains the specific optimisation algorithms for TopasOpt. Most functionality is defined in TopasOptBaseClass,
 which other optimisers inherit from.
 """
-import subprocess
-import jsonpickle
-from matplotlib import pyplot as plt
+import logging
+import os
 # matplotlib.use('Agg')  # if having trouble with generating figures through ssh, this resolves...
 import shutil
-from scipy.optimize import minimize
-from scipy import stats
+import stat
+import subprocess
+import sys
+from abc import abstractmethod
 from pathlib import Path
+
+import jsonpickle
 import numpy as np
-import sys, os
 from bayes_opt import BayesianOptimization
 from bayes_opt import acquisition
+from bayes_opt.event import Events
 from bayes_opt.logger import JSONLogger
 from bayes_opt.util import load_logs, NotUniqueError
-from bayes_opt.event import Events
+from matplotlib import pyplot as plt
+from scipy import stats
+from scipy.optimize import minimize
+from scipy.optimize import rosen
 from sklearn.gaussian_process.kernels import Matern
-import logging
+
 from .utilities import bcolors, FigureSpecs, newJSONLogger, ReadInLogFile, PlotLogFile
-import stat
-from scipy.optimize import rosen
-from abc import abstractmethod
 
 ch = logging.StreamHandler()
 formatter = logging.Formatter('[%(filename)s: line %(lineno)d %(levelname)8s] %(message)s')
diff --git a/pyproject.toml b/pyproject.toml
index 148b1c0..72f8d67 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "topasopt"
-version = "1.0.0"
+version = "1.0.1"
 description = ""
 authors = ["brendan whelan"]
 license = "MIT"
diff --git a/tests/test_optimisers.py b/tests/test_optimisers.py
index 8b401d5..5a4f768 100644
--- a/tests/test_optimisers.py
+++ b/tests/test_optimisers.py
@@ -73,7 +73,7 @@ def test_Nelder_Mead_UserDefinedSimplex():
 
 def test_Bayesian():
     ## Test Bayesian
-    optimisation_params['Nitterations'] = 50
+    optimisation_params['Nitterations'] = 100
     optimisation_params['Suggestions'] = np.array([0.7, 0.7])
 
     Optimiser = to.BayesianOptimiser(optimisation_params=optimisation_params, BaseDirectory=BaseDirectory,