diff --git a/tests/reduction_methods_1D/rational_interpolant_greedy_1d.py b/tests/reduction_methods_1D/rational_interpolant_greedy_1d.py
index 6fb8d82..f5ee31d 100644
--- a/tests/reduction_methods_1D/rational_interpolant_greedy_1d.py
+++ b/tests/reduction_methods_1D/rational_interpolant_greedy_1d.py
@@ -1,99 +1,100 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from matrix_fft import matrixFFT
-from rrompy.reduction_methods.greedy import RationalInterpolantGreedy as RIG
+from rrompy.reduction_methods.standard.greedy import (
+                                              RationalInterpolantGreedy as RIG)
 from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
 
 def test_lax_tolerance(capsys):
     mu = 2.25
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"), "S": 4,
               "polybasis": "CHEBYSHEV", "greedyTol": 1e-2,
               "errorEstimatorKind": "LOOK_AHEAD",
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx = RIG(solver, 4., approx_state = True, approxParameters = params,
                  verbosity = 100)
     approx.setupApprox()
 
     out, err = capsys.readouterr()
     assert "Done computing snapshots (final snapshot count: 10)." in out
     assert len(err) == 0
     assert np.isclose(approx.normErr(mu)[0], 2.169678e-4, rtol = 1e-1)
 
 def test_samples_at_poles():
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"), "S": 4,
               "nTestPoints": 100, "polybasis": "CHEBYSHEV", "greedyTol": 1e-5,
               "errorEstimatorKind": "AFFINE",
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx = RIG(solver, 4., approx_state = True, approxParameters = params,
                  verbosity = 0)
     approx.setupApprox()
     
     for mu in approx.mus:
         assert np.isclose(approx.normErr(mu)[0] / (1e-15+approx.normHF(mu)[0]),
                           0., atol = 1e-4)
 
     poles = approx.getPoles()
     for lambda_ in range(2, 7):
         assert np.isclose(np.min(np.abs(poles - lambda_)), 0., atol = 1e-3)
         assert np.isclose(np.min(np.abs(np.array(approx.mus(0)) - lambda_)),
                           0., atol = 1e-1)
 
 def test_maxIter():
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"),
               "S": 5, "nTestPoints": 500, "polybasis": "CHEBYSHEV",
               "greedyTol": 1e-6, "maxIter": 10,
               "errorEstimatorKind": "INTERPOLATORY",
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx = RIG(solver, 4., approx_state = True, approxParameters = params,
                  verbosity = 0)
     approx.input = lambda: "N"
     approx.setupApprox()
     
     assert len(approx.mus) == 10
     _, _, maxEst = approx.errorEstimator(approx.muTest, True)
     assert maxEst > 1e-6
 
 def test_load_copy(capsys):
     mu = 3.
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"), "S": 4,
               "nTestPoints": 100, "polybasis": "CHEBYSHEV",
               "greedyTol": 1e-5, "errorEstimatorKind": "AFFINE",
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx1 = RIG(solver, 4., approx_state = True, approxParameters = params,
                   verbosity = 100)
     approx1.setupApprox()
     err1 = approx1.normErr(mu)[0]
     out, err = capsys.readouterr()
     assert "Solving HF model for mu =" in out
     assert len(err) == 0
     approx2 = RIG(solver, 4., approx_state = True, approxParameters = params,
                   verbosity = 100)
     approx2.setTrainedModel(approx1)
     approx2.setHF(mu, approx1.uHF)
     err2 = approx2.normErr(mu)[0]
     out, err = capsys.readouterr()
     assert "Solving HF model for mu =" not in out
     assert len(err) == 0
     assert np.isclose(err1, err2, rtol = 1e-10)
 
diff --git a/tests/reduction_methods_1D/reduced_basis_greedy_1d.py b/tests/reduction_methods_1D/reduced_basis_greedy_1d.py
index 53aa7e6..345febd 100644
--- a/tests/reduction_methods_1D/reduced_basis_greedy_1d.py
+++ b/tests/reduction_methods_1D/reduced_basis_greedy_1d.py
@@ -1,60 +1,60 @@
 # Copyright (C) 2018 by the RROMPy authors
 #
 # This file is part of RROMPy.
 #
 # RROMPy is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Lesser General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 #
 # RROMPy is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 # GNU Lesser General Public License for more details.
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
 #
 
 import numpy as np
 from matrix_fft import matrixFFT
-from rrompy.reduction_methods.greedy import ReducedBasisGreedy as RBG
+from rrompy.reduction_methods.standard.greedy import ReducedBasisGreedy as RBG
 from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
 
 def test_lax_tolerance(capsys):
     mu = 2.25
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"),
               "S": 4, "greedyTol": 1e-2,
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx = RBG(solver, 4., approx_state = True, approxParameters = params,
                  verbosity = 10)
     approx.setupApprox()
 
     out, err = capsys.readouterr()
     assert "Done computing snapshots (final snapshot count: 10)." in out
     assert len(err) == 0
 
     assert len(approx.mus) == 10
     _, _, maxEst = approx.errorEstimator(approx.muTest, True)
     assert maxEst < 1e-2
     assert np.isclose(approx.normErr(mu)[0], 1.5056e-05, rtol = 1e-1)
 
 def test_samples_at_poles():
     solver = matrixFFT()
     params = {"POD": True, "sampler": QS([1.5, 6.5], "UNIFORM"),
               "S": 4, "nTestPoints": 100, "greedyTol": 1e-5,
               "trainSetGenerator": QS([1.5, 6.5], "CHEBYSHEV")}
     approx = RBG(solver, 4., approx_state = True, approxParameters = params,
                  verbosity = 0)
     approx.setupApprox()
     
     for mu in approx.mus:
         assert np.isclose(approx.normErr(mu)[0] / (1e-15+approx.normHF(mu)[0]),
                           0., atol = 1e-4)
 
     poles = approx.getPoles()
     for lambda_ in range(2, 7):
         assert np.isclose(np.min(np.abs(poles - lambda_)), 0., atol = 1e-3)
         assert np.isclose(np.min(np.abs(np.array(approx.mus(0)) - lambda_)),
                           0., atol = 1e-1)