test: use flatter import

tests/test_active_learning.py

+import os
 import pytest
 import numpy as np
+import matplotlib.pyplot as plt
 from scipy import optimize
 from scipy.stats import norm, multivariate_normal, uniform
-from psimpy.inference.active_learning import ActiveLearning
-from psimpy.simulator.run_simulator import RunSimulator
-from psimpy.simulator.mass_point_model import MassPointModel
-from psimpy.sampler.latin import LHS
-from psimpy.sampler.saltelli import Saltelli
-from psimpy.emulator.robustgasp import ScalarGaSP, PPGaSP
-from psimpy.inference.bayes_inference import GridEstimation
-from psimpy.inference.bayes_inference import MetropolisHastingsEstimation
-from psimpy.sampler.metropolis_hastings import MetropolisHastings
-from scipy.stats import uniform, norm
-from scipy import optimize
 from beartype.roar import BeartypeCallHintParamViolation
-import matplotlib.pyplot as plt
-import os
+from psimpy.inference import ActiveLearning
+from psimpy.simulator import RunSimulator
+from psimpy.simulator import MassPointModel
+from psimpy.sampler import LHS
+from psimpy.sampler import Saltelli
+from psimpy.emulator import ScalarGaSP, PPGaSP
+from psimpy.inference import GridEstimation
+from psimpy.inference import MetropolisHastingsEstimation
+from psimpy.sampler import MetropolisHastings
 
 dir_test = os.path.abspath(os.path.join(__file__, '../'))
 

tests/test_bayes_inference.py

+import os
 import pytest
+import itertools
 import numpy as np
-from scipy.stats import norm, multivariate_normal, uniform
-from psimpy.inference.bayes_inference import GridEstimation
-from psimpy.inference.bayes_inference import MetropolisHastingsEstimation
-from psimpy.sampler.metropolis_hastings import MetropolisHastings
 import matplotlib.pyplot as plt
-import os
-import itertools
+from scipy.stats import norm, multivariate_normal, uniform
+from psimpy.inference import GridEstimation
+from psimpy.inference import MetropolisHastingsEstimation
+from psimpy.sampler import MetropolisHastings
 
 dir_test = os.path.abspath(os.path.join(__file__, '../'))
 

tests/test_latin.py

 import pytest
 import numpy as np
-from psimpy.sampler.latin import LHS
+from psimpy.sampler import LHS
 from beartype.roar import BeartypeCallHintParamViolation
 
 @pytest.mark.parametrize(

tests/test_mass_point_model.py

-from psimpy.simulator.mass_point_model import MassPointModel
-import numpy as np
 import os
+import numpy as np
 import pytest
+from psimpy.simulator import MassPointModel
 
 @pytest.mark.parametrize(
     "elevation, x0, y0",

tests/test_metropolis_hastings.py

+import os
 import pytest
 import numpy as np
-from scipy.stats import norm, multivariate_normal, uniform
-from psimpy.sampler.metropolis_hastings import MetropolisHastings
 import matplotlib.pyplot as plt
-import os
+from scipy.stats import norm, multivariate_normal, uniform
+from psimpy.sampler import MetropolisHastings
 
 @pytest.mark.parametrize(
     "ndim, init_state, f_sample, target, ln_target, bounds, f_density, symmetric",

tests/test_ravaflow24.py

-from psimpy.simulator.ravaflow24 import Ravaflow24Mixture
-import numpy as np
 import os
 import pytest
+import numpy as np
+from psimpy.simulator import Ravaflow24Mixture
 
 @pytest.mark.parametrize(
     "dir_sim, conversion_control, curvature_control, surface_control, \

tests/test_robustgasp.py

+import os
 import pytest
 import numpy as np
-from psimpy.emulator.robustgasp import RobustGaSPBase, ScalarGaSP, PPGaSP
-from beartype.roar import BeartypeCallHintParamViolation
 from rpy2.rinterface import NA
-import os
+from beartype.roar import BeartypeCallHintParamViolation
+from psimpy.emulator import ScalarGaSP, PPGaSP
+from psimpy.emulator.robustgasp import RobustGaSPBase
 
 @pytest.mark.parametrize(
     "ndim, zero_mean, nugget, nugget_est, range_par, method, prior_choice, \

tests/test_run_mass_point_model.py

-from psimpy.simulator.run_simulator import RunSimulator
-from psimpy.simulator.mass_point_model import MassPointModel
 import os
-import numpy as np
-import itertools
 import time
+import itertools
+import numpy as np
+from psimpy.simulator import RunSimulator
+from psimpy.simulator import MassPointModel
 
 def test_run_mass_point_model():
     mpm = MassPointModel()

tests/test_run_ravaflow24.py

-from psimpy.simulator.run_simulator import RunSimulator
-from psimpy.simulator.ravaflow24 import Ravaflow24Mixture
-import numpy as np
-import itertools
-import time
 import os
+import time
+import itertools
+import numpy as np
+from psimpy.simulator import RunSimulator
+from psimpy.simulator import Ravaflow24Mixture
 
 dir_test = os.path.abspath(os.path.join(__file__, '../'))
 

tests/test_run_simulator.py

-from psimpy.simulator.run_simulator import RunSimulator
+import os
 import pytest
 import numpy as np
-import os
 from beartype.roar import BeartypeCallHintParamViolation
+from psimpy.simulator import RunSimulator
 
 def add(a, b, c , d=100, save=False, filename=None):
     if save is True:

tests/test_saltelli.py

 import pytest
 import numpy as np
 from SALib.sample.saltelli import sample
-from psimpy.sampler.saltelli import Saltelli
+from psimpy.sampler import Saltelli
 from beartype.roar import BeartypeCallHintParamViolation
 
 

tests/test_sobol.py

 import pytest
 import numpy as np
-from psimpy.sampler.saltelli import Saltelli
-from psimpy.sampler.latin import LHS
-from psimpy.emulator.robustgasp import ScalarGaSP
-from psimpy.sensitivity.sobol import SobolAnalyze
-
+from psimpy.sampler import Saltelli
+from psimpy.sampler import LHS
+from psimpy.emulator import ScalarGaSP
+from psimpy.sensitivity import SobolAnalyze
 
 def f(x1,x2,x3):
     return np.sin(x1) + 7*np.sin(x2)**2 + 0.1*x3**4*np.sin(x1)
@@ -42,7 +41,6 @@ def test_SobolAnalyze(calc_second_order, skip_values, nbase, seed, mode,
     print('analytical S1: \n', [0.314, 0.442, 0])
 
 
-
 @pytest.mark.parametrize(
     "calc_second_order, skip_values, nbase, seed, mode, max_workers",
     [

tests/test_util_funcs.py

-import numpy as np
 import pytest
+import numpy as np
+from beartype.roar import BeartypeCallHintParamViolation
 from psimpy.utility.util_funcs import scale_samples
-import beartype
+
 
 @pytest.mark.parametrize(
     'samples, bounds',
@@ -11,7 +12,7 @@ import beartype
      ]
 )
 def test_scale_samples_TypeError(samples, bounds):
-    with pytest.raises(beartype.roar.BeartypeCallHintParamViolation):
+    with pytest.raises(BeartypeCallHintParamViolation):
         scale_samples(samples, bounds)
 
 @pytest.mark.parametrize(