Adjust ruff configuration to handle trailing whitespaces (#524)

* Adjust ruff conf to fix whitespace related lints

* Fix whitespace-related lints (W)

* Fix MFK without noise link and make some sub-sections

* Enforce line not longer than 120 chars

* Wrap lines longer than 120 chars

doc/preprocess.py

@@ -1,6 +1,6 @@
 """
 Author: Dr. John T. Hwang <hwangjt@umich.edu>
-        
+
 This package is distributed under New BSD license.
 """
 

doc/preprocess_test.py

@@ -1,6 +1,6 @@
 """
 Author: Dr. John T. Hwang <hwangjt@umich.edu>
-        
+
 This package is distributed under New BSD license.
 """
 

pyproject.toml

@@ -2,7 +2,11 @@
 requires = ["setuptools", "wheel", "numpy", "Cython"]
 
 [tool.ruff.lint]
+select = ["E", "F", "W"]
 ignore = []
 
+[tool.ruff.lint.pycodestyle]
+max-line-length = 120
+
 [tool.pytest.ini_options]
 filterwarnings = ["ignore:FigureCanvasAgg is non-interactive:UserWarning"]

setup.py

@@ -4,7 +4,7 @@
         Remi Lafage <remi.lafage@onera.fr>
         Lucas Alber <lucasd.alber@gmail.com>
         Paul Saves <paul.saves@onera.fr>
-        
+
 This package is distributed under New BSD license.
 """
 
@@ -33,19 +33,19 @@
 """
 
 LONG_DESCRIPTION = """
-The surrogate modeling toolbox (SMT) is a Python package that contains 
-a collection of surrogate modeling methods, sampling techniques, and 
-benchmarking functions. This package provides a library of surrogate 
-models that is simple to use and facilitates the implementation of additional methods. 
+The surrogate modeling toolbox (SMT) is a Python package that contains \
+a collection of surrogate modeling methods, sampling techniques, and \
+benchmarking functions. This package provides a library of surrogate \
+models that is simple to use and facilitates the implementation of additional methods. \
 
-SMT is different from existing surrogate modeling libraries because of 
-its emphasis on derivatives, including training derivatives used for 
-gradient-enhanced modeling, prediction derivatives, and derivatives 
-with respect to the training data. It also includes new surrogate models 
-that are not available elsewhere: kriging by partial-least squares reduction 
+SMT is different from existing surrogate modeling libraries because of \
+its emphasis on derivatives, including training derivatives used for \
+gradient-enhanced modeling, prediction derivatives, and derivatives \
+with respect to the training data. It also includes new surrogate models \
+that are not available elsewhere: kriging by partial-least squares reduction \
 and energy-minimizing spline interpolation.
 
-SMT 2.0 adds the capability to handle mixed-variable surrogate models 
+SMT 2.0 adds the capability to handle mixed-variable surrogate models \
 and hierarchical variables.
 """
 
@@ -111,7 +111,7 @@
         "scikit-learn",
         "pyDOE3",
         "scipy",
-        "jenn", 
+        "jenn",
     ],
     extras_require={
         "numba": [  # pip install smt[numba]

smt/applications/mfk.py

@@ -64,7 +64,8 @@ def __init__(self, nlevel, xlimits=None, design_space=None, random_state=None):
             )
         elif xlimits is not None and design_space is not None:
             raise ValueError(
-                "Use either design_space for mixed inputs or xlimits for continuous one. Please avoid overspecification."
+                "Use either design_space for mixed inputs or xlimits for continuous one. \
+                    Please avoid overspecification."
             )
         elif xlimits is not None:
             self.design_space = ensure_design_space(xlimits=xlimits)
@@ -243,7 +244,8 @@ def _new_train_init(self):
         if self.name in ["MFKPLS", "MFKPLSK"]:
             _pls = pls(self.options["n_comp"])
 
-            # As of sklearn 0.24.1 PLS with zeroed outputs raises an exception while sklearn 0.23 returns zeroed x_rotations
+            # As of sklearn 0.24.1 PLS with zeroed outputs raises an exception
+            # while sklearn 0.23 returns zeroed x_rotations
             # For now the try/except below is a workaround to restore the 0.23 behaviour
             try:
                 # PLS is done on the highest fidelity identified by the key None
@@ -1027,7 +1029,8 @@ def _check_param(self):
                     self.options["theta0"] *= np.ones((1, n_param))
                 else:
                     raise ValueError(
-                        "the length of theta0 (%s) should be equal to the number of dim (%s) or levels of fidelity (%s)."
+                        "the length of theta0 (%s) should be equal to the number of dim (%s) \
+                            or levels of fidelity (%s)."
                         % (len(self.options["theta0"]), n_param, self.nlvl)
                     )
             else:
@@ -1050,7 +1053,8 @@ def _check_param(self):
                             self.options["noise0"][i] *= np.ones(self.nt_all[i])
                         else:
                             raise ValueError(
-                                "for the level of fidelity %s, the length of noise0 (%s) should be equal to the number of observations (%s)."
+                                "for the level of fidelity %s, the length of noise0 (%s) should be equal to \
+                                    the number of observations (%s)."
                                 % (i, len(self.options["noise0"][i]), self.nt_all[i])
                             )
             else:

smt/applications/mixed_integer.py

@@ -226,7 +226,8 @@ def __init__(
                 "categorical_kernel"
             ] = MixIntKernelType.HOMO_HSPHERE
             warnings.warn(
-                "Using MixedIntegerSurrogateModel integer model with Continuous Relaxation is not supported. Switched to homoscedastic hypersphere kernel instead."
+                "Using MixedIntegerSurrogateModel integer model with Continuous Relaxation is not supported. \
+                    Switched to homoscedastic hypersphere kernel instead."
             )
         if self._surrogate.options["categorical_kernel"] is not None:
             self._input_in_folded_space = False

smt/applications/tests/test_mfk_variance.py

@@ -4,9 +4,9 @@
 
 @author: Vincent Drouet and Nathalie Bartoli
 in order to validate the variance formula for multifidelity on the branin 2D function
-Comparisons are based  on the paper Le Gratiet et Cannamela 2015  :
-    
-Le Gratiet, L., & Cannamela, C. (2015). 
+Comparisons are based  on the paper Le Gratiet et Cannamela 2015 :
+
+Le Gratiet, L., & Cannamela, C. (2015).
 Cokriging-based sequential design strategies using fast cross-validation
 techniques for multi-fidelity computer codes. Technometrics, 57(3), 418-427.
 https://doi.org/10.1080/00401706.2014.928233

smt/examples/run_examples.py

@@ -82,7 +82,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the LS model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the LS model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -134,7 +135,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the QP model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the QP model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -187,7 +189,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the Kriging model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the Kriging model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -243,7 +246,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the KPLS model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the KPLS model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 # Prediction of the derivatives with regards to each direction space
@@ -311,7 +315,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the KPLSK model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the KPLSK model (from left to right then from top to bottom): \
+        validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -375,7 +380,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the GEKPLS1 model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the GEKPLS1 model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -439,7 +445,8 @@
     plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
     plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
     plt.suptitle(
-        "Validation of the GEKPLS2 model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+        "Validation of the GEKPLS2 model (from left to right then from top to bottom): \
+            validation of the prediction model and the i-th prediction of the derivative---i=1:10"
     )
 
 
@@ -510,7 +517,8 @@
         plt.setp(axarr[3, 2].get_xticklabels(), visible=False)
         plt.setp(axarr[3, 2].get_yticklabels(), visible=False)
         plt.suptitle(
-            "Validation of the RBF model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:10"
+            "Validation of the RBF model (from left to right then from top to bottom): \
+                validation of the prediction model and the i-th prediction of the derivative---i=1:10"
         )
 
     # Prediction of the derivatives with regards to each direction space
@@ -596,7 +604,8 @@
         plt.setp(axarr[2, 0].get_xticklabels(), visible=False)
         plt.setp(axarr[2, 0].get_yticklabels(), visible=False)
         plt.suptitle(
-            "Validation of the RMTB model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:3"
+            "Validation of the RMTB model (from left to right then from top to bottom): \
+                validation of the prediction model and the i-th prediction of the derivative---i=1:3"
         )
 
     # Prediction of the derivatives with regards to each direction space
@@ -658,7 +667,8 @@
         plt.setp(axarr[2, 0].get_xticklabels(), visible=False)
         plt.setp(axarr[2, 0].get_yticklabels(), visible=False)
         plt.suptitle(
-            "Validation of the RMTC model (from left to right then from top to bottom): validation of the prediction model and the i-th prediction of the derivative---i=1:3"
+            "Validation of the RMTC model (from left to right then from top to bottom): \
+                validation of the prediction model and the i-th prediction of the derivative---i=1:3"
         )
 
     # Prediction of the derivatives with regards to each direction space

smt/problems/cantilever_beam.py

@@ -4,8 +4,14 @@
 This package is distributed under New BSD license.
 
 Cantilever beam problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
-Cheng, G. H., Younis, A., Hajikolaei, K. H., and Wang, G. G. Trust Region Based Mode Pursuing Sampling Method for Global Optimization of High Dimensional Design Problems. Journal of Mechanical Design, 137(2). 2015.
+
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
+Cheng, G. H., Younis, A., Hajikolaei, K. H., and Wang, G. G.
+Trust Region Based Mode Pursuing Sampling Method for Global Optimization of High Dimensional Design Problems.
+Journal of Mechanical Design, 137(2). 2015.
 """
 
 import numpy as np

smt/problems/hierarchical_goldstein.py

@@ -3,7 +3,8 @@
 This package is distributed under New BSD license.
 
 Cantilever beam problem from:
-P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier. A mixed-categorical correlation kernel for gaussian process, 2022
+P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier. A mixed-categorical correlation kernel
+for gaussian process, 2022
 """
 
 import numpy as np

smt/problems/mixed_cantilever_beam.py

@@ -1,9 +1,10 @@
 """
-Author: P.Saves 
+Author: P.Saves
 This package is distributed under New BSD license.
 
 Cantilever beam problem from:
-P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier. A mixed-categorical correlation kernel for gaussian process, 2022
+P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier.
+A mixed-categorical correlation kernel for gaussian process, 2022
 """
 
 import numpy as np

smt/problems/neural_network.py

@@ -1,11 +1,12 @@
 """
-Author: P.Saves 
+Author: P.Saves
 This package is distributed under New BSD license.
 
 Multi-Layer Perceptron problem from:
-C. Audet, E. Hall e-Hannan, and S. Le Digabel. A general mathematical framework for constrained mixed-variable blackbox optimization problems with meta and categorical variables. Operations Research Forum,499
-4:137, 2023.
-    """
+C. Audet, E. Hall e-Hannan, and S. Le Digabel. A general mathematical framework for
+constrained mixed-variable blackbox optimization problems with meta and categorical variables.
+Operations Research Forum,4994:137, 2023.
+"""
 
 import numpy as np
 

smt/problems/robot_arm.py

@@ -4,7 +4,10 @@
 This package is distributed under New BSD license.
 
 Robot arm problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
 An, J., and Owen, A. Quasi-Regression. Journal of complexity, 17(4), pp. 588-607, 2001.
 """
 

smt/problems/torsion_vibration.py

@@ -5,8 +5,14 @@
 This package is distributed under New BSD license.
 
 Torsion vibration problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
-Wang, L., Beeson, D., Wiggs, G., and Rayasam, M. A Comparison of Metamodeling Methods Using Practical Industry Requirements. In Proceedings of the 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Newport, RI, pp. AIAA 2006-1811.
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
+Wang, L., Beeson, D., Wiggs, G., and Rayasam, M.
+A Comparison of Metamodeling Methods Using Practical Industry Requirements.
+In Proceedings of the 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics,
+and materials conference, Newport, RI, pp. AIAA 2006-1811.
 """
 
 import numpy as np

smt/problems/water_flow.py

@@ -5,8 +5,12 @@
 This package is distributed under New BSD license.
 
 Water flow problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
-Morris, M. D., Mitchell, T. J., and Ylvisaker, D. Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction. Technometrics, 35(3), pp. 243-255. 1993.
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
+Morris, M. D., Mitchell, T. J., and Ylvisaker, D. Bayesian Design and Analysis of Computer Experiments:
+Use of Derivatives in Surface Prediction. Technometrics, 35(3), pp. 243-255. 1993.
 """
 
 import numpy as np

smt/problems/water_flow_lfidelity.py

@@ -3,7 +3,8 @@
         Dr. John T. Hwang         <hwangjt@umich.edu>
 
 Water flow problem from:
-Xiong, S., Qian, P. Z., & Wu, C. J. (2013). Sequential design and analysis of high-accuracy and low-accuracy computer codes. Technometrics, 55(1), 37-46.
+Xiong, S., Qian, P. Z., & Wu, C. J. (2013). Sequential design and analysis of high-accuracy
+and low-accuracy computer codes. Technometrics, 55(1), 37-46.
 """
 
 import numpy as np

smt/problems/welded_beam.py

@@ -5,8 +5,12 @@
 This package is distributed under New BSD license.
 
 Welded beam problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
-Deb, K. An Efficient Constraint Handling Method for Genetic Algorithms. Computer methods in applied mechanics and engineering, 186(2), pp. 311-338. 2000.
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
+Deb, K. An Efficient Constraint Handling Method for Genetic Algorithms.
+Computer methods in applied mechanics and engineering, 186(2), pp. 311-338. 2000.
 """
 
 import numpy as np

smt/problems/wing_weight.py

@@ -5,7 +5,10 @@
 This package is distributed under New BSD license.
 
 Aircraft wing weight problem from:
-Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design: comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers. June, 2016.
+Liu, H., Xu, S., & Wang, X. Sampling strategies and metamodeling techniques for engineering design:
+comparison and application. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition.
+American Society of Mechanical Engineers. June, 2016.
+
 Forrester, A., Sobester, A., and Keane, A., 2008,
 Engineering Design Via Surrogate Modelling: A Practical Guide, John Wiley & Sons, United Kingdom.
 """

smt/sampling_methods/pydoe.py

@@ -100,7 +100,8 @@ def _initialize(self, **kwargs):
             "reduction",
             types=int,
             default=2,
-            desc="Reduction factor (bigger than 1). Larger `reduction` means fewer experiments in the design and more possible complementary designs",
+            desc="Reduction factor (bigger than 1). Larger `reduction` means fewer experiments \
+                  in the design and more possible complementary designs",
         )
 
     def _compute_doe(self):