BLD: increase minimum supported NumPy/SciPy versions to 1.23 and 1.8

azure-pipelines.yml

@@ -82,7 +82,7 @@ stages:
           displayName: 'Install dependencies'
         - script: |
             python -m pip install --upgrade build pip wheel
-            python -m pip install asteval==0.9.28 numpy==1.19.0 scipy==1.6.0 uncertainties==3.1.4
+            python -m pip install asteval==0.9.28 numpy==1.23.0 scipy==1.8.0 uncertainties==3.1.4
           displayName: 'Install minimum required version of dependencies'
         - script: |
             python -m build

doc/installation.rst

@@ -36,8 +36,8 @@ Lmfit works with `Python`_ versions 3.8 and higher. Version
 0.9.15 is the final version to support Python 2.7.
 
 Lmfit requires the following Python packages, with versions given:
-   * `NumPy`_ version 1.19 or higher.
-   * `SciPy`_ version 1.6 or higher.
+   * `NumPy`_ version 1.23 or higher.
+   * `SciPy`_ version 1.8 or higher.
    * `asteval`_ version 0.9.28 or higher.
    * `uncertainties`_ version 3.1.4 or higher.
 

lmfit/minimizer.py

@@ -22,7 +22,6 @@
 import warnings
 
 import numpy as np
-from scipy import __version__ as scipy_version
 from scipy.linalg import LinAlgError, inv
 from scipy.optimize import basinhopping as scipy_basinhopping
 from scipy.optimize import brute as scipy_brute
@@ -1735,12 +1734,8 @@ def basinhopping(self, params=None, max_nfev=None, **kws):
         basinhopping_kws = dict(niter=100, T=1.0, stepsize=0.5,
                                 minimizer_kwargs=None, take_step=None,
                                 accept_test=None, callback=None, interval=50,
-                                disp=False, niter_success=None, seed=None)
-
-        # FIXME: update when SciPy requirement is >= 1.8
-        if int(scipy_version.split('.')[1]) >= 8:
-            basinhopping_kws.update({'target_accept_rate': 0.5,
-                                     'stepwise_factor': 0.9})
+                                disp=False, niter_success=None, seed=None,
+                                target_accept_rate=0.5, stepwise_factor=0.9)
 
         basinhopping_kws.update(self.kws)
         basinhopping_kws.update(kws)
@@ -2118,14 +2113,10 @@ def shgo(self, params=None, max_nfev=None, **kws):
 
         self.set_max_nfev(max_nfev, 200000*(result.nvarys+1))
 
-        shgo_kws = dict(constraints=None, n=100, iters=1, callback=None,
+        shgo_kws = dict(constraints=None, n=None, iters=1, callback=None,
                         minimizer_kwargs=None, options=None,
                         sampling_method='simplicial')
 
-        # FIXME: update when SciPy requirement is >= 1.7
-        if int(scipy_version.split('.')[1]) >= 7:
-            shgo_kws['n'] = None
-
         shgo_kws.update(self.kws)
         shgo_kws.update(kws)
 
@@ -2196,19 +2187,14 @@ def dual_annealing(self, params=None, max_nfev=None, **kws):
         result.method = 'dual_annealing'
         self.set_max_nfev(max_nfev, 200000*(result.nvarys+1))
 
-        da_kws = dict(maxiter=1000, local_search_options={},
-                      initial_temp=5230.0, restart_temp_ratio=2e-05,
-                      visit=2.62, accept=-5.0, maxfun=2*self.max_nfev,
-                      seed=None, no_local_search=False, callback=None, x0=None)
+        da_kws = dict(maxiter=1000, minimizer_kwargs=None, initial_temp=5230.0,
+                      restart_temp_ratio=2e-05, visit=2.62, accept=-5.0,
+                      maxfun=2*self.max_nfev, seed=None, no_local_search=False,
+                      callback=None, x0=None)
 
         da_kws.update(self.kws)
         da_kws.update(kws)
 
-        # FIXME: update when SciPy requirement is >= 1.8
-        # ``local_search_options`` deprecated in favor of ``minimizer_kwargs``
-        if int(scipy_version.split('.')[1]) >= 8:
-            da_kws.update({'minimizer_kwargs': da_kws.pop('local_search_options')})
-
         varying = np.asarray([par.vary for par in self.params.values()])
         bounds = np.asarray([(par.min, par.max) for par in
                              self.params.values()])[varying]

setup.cfg

@@ -35,8 +35,8 @@ python_requires = >=3.8
 setup_requires = setuptools_scm
 install_requires =
     asteval>=0.9.28
-    numpy>=1.19
-    scipy>=1.6
+    numpy>=1.23
+    scipy>=1.8
     uncertainties>=3.1.4
 
 [options.packages.find]

tests/test_ampgo.py

@@ -3,7 +3,6 @@
 import numpy as np
 from numpy.testing import assert_allclose
 import pytest
-from scipy import __version__ as scipy_version
 
 import lmfit
 from lmfit._ampgo import ampgo, tunnel
@@ -60,15 +59,7 @@ def test_ampgo_local_solver(minimizer_Alpine02):
     """Test AMPGO algorithm with local solver."""
     kws = {'local': 'Nelder-Mead'}
 
-    # bounds in Nelder-Mead are supported since SciPy v1.7.0
-    # FIXME: clean this up after we require SciPy >= 1.7.0
-    if int(scipy_version.split('.')[1]) < 7:
-        msg = r'Method Nelder-Mead cannot handle constraints nor bounds'
-        with pytest.warns(RuntimeWarning, match=msg):
-            out = minimizer_Alpine02.minimize(method='ampgo', **kws)
-    else:
-        out = minimizer_Alpine02.minimize(method='ampgo', **kws)
-
+    out = minimizer_Alpine02.minimize(method='ampgo', **kws)
     out_x = np.array([out.params['x0'].value, out.params['x1'].value])
 
     assert 'ampgo' and 'Nelder-Mead' in out.method

tests/test_basinhopping.py

@@ -2,7 +2,6 @@
 import numpy as np
 from numpy.testing import assert_allclose
 import pytest
-from scipy import __version__ as scipy_version
 from scipy.optimize import basinhopping
 
 import lmfit
@@ -62,11 +61,6 @@ def residual_2d(params):
     assert_allclose(out.params['x0'].value, ret.x[0], rtol=1e-5)
     assert_allclose(out.params['x1'].value, ret.x[1], rtol=1e-5)
 
-    # FIXME: update when SciPy requirement is >= 1.8
-    if int(scipy_version.split('.')[1]) >= 8:
-        assert 'target_accept_rate' in out.call_kws
-        assert 'stepwise_factor' in out.call_kws
-
 
 def test_basinhopping_Alpine02(minimizer_Alpine02):
     """Test basinhopping on Alpine02 function."""

tests/test_dual_annealing.py

@@ -3,7 +3,6 @@
 import numpy as np
 from numpy.testing import assert_allclose
 import scipy
-from scipy import __version__ as scipy_version
 
 import lmfit
 
@@ -54,13 +53,6 @@ def test_da_Alpine02(minimizer_Alpine02):
     assert_allclose(max(out_x), max(global_optimum), rtol=1e-3)
     assert out.method == 'dual_annealing'
 
-    # FIXME: update when SciPy requirement is >= 1.8
-    # ``local_search_options`` deprecated in favor of ``minimizer_kwargs``
-    if int(scipy_version.split('.')[1]) >= 8:
-        assert 'minimizer_kwargs' in out.call_kws
-    else:
-        assert 'local_search_options' in out.call_kws
-
 
 def test_da_bounds(minimizer_Alpine02):
     """Test dual_annealing algorithm with bounds."""

tests/test_shgo.py

@@ -4,7 +4,6 @@
 from numpy.testing import assert_allclose
 import pytest
 import scipy
-from scipy import __version__ as scipy_version
 
 import lmfit
 
@@ -82,12 +81,6 @@ def test_shgo_sobol_Alpine02(minimizer_Alpine02):
     assert_allclose(min(out_x), min(global_optimum), rtol=1e-3)
     assert_allclose(max(out_x), max(global_optimum), rtol=1e-3)
 
-    # FIXME: update when SciPy requirement is >= 1.7
-    if int(scipy_version.split('.')[1]) >= 7:
-        assert out.call_kws['n'] is None
-    else:
-        assert out.call_kws['n'] == 100
-
 
 def test_shgo_bounds(minimizer_Alpine02):
     """Test SHGO algorithm with bounds."""