Set explicit fit('leastsq') on other tests, fix failing tests

examples/hyperspy_as_library/curve_fitting_data.py

@@ -19,7 +19,7 @@
 
 m = s.create_model(ll=ll)
 m.enable_fine_structure()
-m.multifit(kind="smart")
+m.multifit(fitter='leastsq', kind="smart")
 m.plot()
 
 plt.savefig("model_original_spectrum_plot.png")

hyperspy/_components/expression.py

@@ -158,10 +158,11 @@ class docstring.
                 name, sympy.latex(_parse_substitutions(expression)))
 
         for para in self.parameters:
-            para._is_linear = check_parameter_linearity(expression, para.name)
+            para._is_linear = check_parameter_linearity(self._parsed_expr, para.name)
 
     def compile_function(self, module="numpy", position=False):
         expr = _parse_substitutions(self._str_expression)
+        self._parsed_expr = expr
         # Extract x
         x, = [symbol for symbol in expr.free_symbols if symbol.name == "x"]
         # Extract y

hyperspy/datasets/artificial_data.py

@@ -209,7 +209,7 @@ def get_core_loss_eels_model():
     """
     s = get_core_loss_eels_signal()
     m = s.create_model(auto_background=False, GOS='hydrogenic')
-    m.fit()
+    m.fit(fitter='leastsq')
     return m
 
 

hyperspy/tests/drawing/test_plot_model.py

@@ -114,7 +114,7 @@ def set_gaussian(gaussian, centre, sigma):
                                        sigma_value_gaussian):
         set_gaussian(gaussian, centre, sigma)
 
-    m.fit()
+    m.fit(fitter='leastsq')
     m.plot(plot_components=plot_component)
 
     def A_value(s, component, binned):
@@ -145,6 +145,6 @@ def test_fit_EELS_convolved(convolved):
     cl.metadata.General.title = 'Convolved: {}'.format(convolved)
     ll = hs.load(os.path.join(dname, 'Cr_L_ll.hspy')) if convolved else None
     m = cl.create_model(auto_background=False, ll=ll, GOS='hydrogenic')
-    m.fit(kind='smart')
+    m.fit(fitter='leastsq', kind='smart')
     m.plot(plot_components=True)
     return m._plot.signal_plot.figure

hyperspy/tests/model/test_chi_squared.py

@@ -38,7 +38,7 @@ def test_chisq_with_fit(self):
         m = self.model
         g = Gaussian()
         m.append(g)
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.allclose(m.chisq(), 7.78966223)
 
     def test_dof_with_fit(self):
@@ -47,14 +47,14 @@ def test_dof_with_fit(self):
         g1 = Gaussian()
         m.extend((g, g1))
         g1.set_parameters_not_free('A')
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.equal(m.dof(), 5)
 
     def test_red_chisq_with_fit(self):
         m = self.model
         g = Gaussian()
         m.append(g)
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.allclose(m.red_chisq(), 1.55793245)
 
     def test_chisq(self):
@@ -94,7 +94,7 @@ def test_chisq_in_range(self):
         g = Gaussian()
         m.append(g)
         m.set_signal_range(1, 7)
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.allclose(m.red_chisq(), 2.87544335)
 
     def test_chisq_with_inactive_components(self):
@@ -104,7 +104,7 @@ def test_chisq_with_inactive_components(self):
         m.append(ga)
         m.append(gin)
         gin.active = False
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.allclose(m.chisq(), 7.78966223)
 
     def test_dof_with_inactive_components(self):
@@ -114,5 +114,5 @@ def test_dof_with_inactive_components(self):
         m.append(ga)
         m.append(gin)
         gin.active = False
-        m.fit()
+        m.fit(fitter='leastsq')
         assert np.equal(m.dof(), 3)

hyperspy/tests/model/test_component.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from hyperspy.component import Component
-from hyperspy.axes import AxesManager
+from hyperspy.axes import AxesManager, DataAxis
 from unittest import mock
 
 
@@ -202,17 +202,18 @@ def setup_method(self, method):
         c.model = mock.MagicMock()
         c.model.__call__ = mock.MagicMock()
         c.model.channel_switches = np.array([True, False, True])
-        c.model.axis.axis = np.array([0.1, 0.2, 0.3])
+        c.model.axes_manager.signal_axes = [DataAxis(size=3, offset=0.1, scale=0.1)]
+        c.model.axis.axis = c.model.axes_manager.signal_axes[0].axis
         c.function = mock.MagicMock()
-        c.function.return_value = np.array([1.3, ])
+        c.function.return_value = np.array([1.3, 1.3, 1.3])
         c.model.signal.axes_manager.signal_axes = [mock.MagicMock(), ]
         c.model.signal.axes_manager.signal_axes[0].scale = 2.
 
     def test_call(self):
         c = self.c
-        assert 1.3 == c()
-        np.testing.assert_array_equal(c.function.call_args[0][0],
-                                      np.array([0.1, 0.3]))
+        assert (1.3 == c()).all()
+        np.testing.assert_array_almost_equal(c.function.call_args[0][0],
+                                                np.array([0.1, 0.2, 0.3]))
 
     def test_plotting_not_active_component(self):
         c = self.c

hyperspy/tests/model/test_components.py

@@ -289,7 +289,7 @@ def test_both_unbinned(self):
         m.append(fp)
         with ignore_warning(message="invalid value encountered in sqrt",
                             category=RuntimeWarning):
-            m.fit()
+            m.fit(fitter='leastsq')
         assert abs(fp.yscale.value - 100) <= 0.1
 
     def test_both_binned(self):
@@ -302,7 +302,7 @@ def test_both_binned(self):
         m.append(fp)
         with ignore_warning(message="invalid value encountered in sqrt",
                             category=RuntimeWarning):
-            m.fit()
+            m.fit(fitter='leastsq')
         assert abs(fp.yscale.value - 100) <= 0.1
 
     def test_pattern_unbinned_signal_binned(self):
@@ -315,7 +315,7 @@ def test_pattern_unbinned_signal_binned(self):
         m.append(fp)
         with ignore_warning(message="invalid value encountered in sqrt",
                             category=RuntimeWarning):
-            m.fit()
+            m.fit(fitter='leastsq')
         assert abs(fp.yscale.value - 1000) <= 1
 
     def test_pattern_binned_signal_unbinned(self):
@@ -328,7 +328,7 @@ def test_pattern_binned_signal_unbinned(self):
         m.append(fp)
         with ignore_warning(message="invalid value encountered in sqrt",
                             category=RuntimeWarning):
-            m.fit()
+            m.fit(fitter='leastsq')
         assert abs(fp.yscale.value - 10) <= .1
 
 

hyperspy/tests/model/test_edsmodel.py

@@ -26,7 +26,7 @@ def setup_method(self, method):
     def test_fit(self):
         s = self.s
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         np.testing.assert_allclose([i.data for i in
                                     m.get_lines_intensity()],
                                    [[0.5], [0.2], [0.3]], atol=10 - 4)
@@ -84,8 +84,8 @@ def test_temmodel_store(self):
     def test_calibrate_energy_resolution(self):
         s = self.s
         m = s.create_model()
-        m.fit()
-        m.fit_background()
+        m.fit(fitter='leastsq')
+        m.fit_background(fitter='leastsq')
         reso = s.metadata.Acquisition_instrument.TEM.Detector.EDS.\
             energy_resolution_MnKa,
         s.set_microscope_parameters(energy_resolution_MnKa=150)
@@ -97,7 +97,7 @@ def test_calibrate_energy_resolution(self):
     def test_calibrate_energy_scale(self):
         s = self.s
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         ax = s.axes_manager[-1]
         scale = ax.scale
         ax.scale += 0.0004
@@ -107,7 +107,7 @@ def test_calibrate_energy_scale(self):
     def test_calibrate_energy_offset(self):
         s = self.s
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         ax = s.axes_manager[-1]
         offset = ax.offset
         ax.offset += 0.04
@@ -117,7 +117,7 @@ def test_calibrate_energy_offset(self):
     def test_calibrate_xray_energy(self):
         s = self.s
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         m['Fe_Ka'].centre.value = 6.39
         m.calibrate_xray_lines(calibrate='energy', xray_lines=['Fe_Ka'],
                                bound=100)
@@ -135,7 +135,7 @@ def test_calibrate_xray_weight(self):
                          'offset': 4.9})
         s = (s + s1 / 50)
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         with assert_warns(message='The X-ray line expected to be in the model '
                           'was not found'):
             m.calibrate_xray_lines(calibrate='sub_weight',
@@ -147,7 +147,7 @@ def test_calibrate_xray_weight(self):
     def test_calibrate_xray_width(self):
         s = self.s
         m = s.create_model()
-        m.fit()
+        m.fit(fitter='leastsq')
         sigma = m['Fe_Ka'].sigma.value
         m['Fe_Ka'].sigma.value = 0.065
         m.calibrate_xray_lines(calibrate='energy', xray_lines=['Fe_Ka'],
@@ -205,7 +205,7 @@ def setup_method(self, method):
     def test_lines_intensity(self):
         s = self.s
         m = s.create_model()
-        m.multifit()    # m.fit() is just too inaccurate
+        m.multifit(fitter='leastsq')    # m.fit() is just too inaccurate
         ws = np.array([0.5, 0.7, 0.3, 0.5])
         w = np.zeros((4,) + self.mix.shape)
         for x in range(self.mix.shape[0]):

hyperspy/tests/model/test_eelsmodel.py

@@ -176,15 +176,15 @@ def setup_method(self, method):
         self.m.append(hs.model.components1D.Offset())
 
     def test_fit_background_B_C(self):
-        self.m.fit_background()
+        self.m.fit_background(fitter='leastsq')
         assert_allclose(self.m["Offset"].offset.value,
                         1)
         assert self.m["B_K"].active
         assert self.m["C_K"].active
 
     def test_fit_background_C(self):
         self.m["B_K"].active = False
-        self.m.fit_background()
+        self.m.fit_background(fitter='leastsq')
         assert_allclose(self.m["Offset"].offset.value,
                         1.71212121212)
         assert not self.m["B_K"].active
@@ -193,7 +193,7 @@ def test_fit_background_C(self):
     def test_fit_background_no_edge(self):
         self.m["B_K"].active = False
         self.m["C_K"].active = False
-        self.m.fit_background()
+        self.m.fit_background(fitter='leastsq')
         assert_allclose(self.m["Offset"].offset.value,
                         2.13567839196)
         assert not self.m["B_K"].active

hyperspy/tests/model/test_fancy_indexing.py

@@ -48,7 +48,7 @@ def setup_method(self, method):
         m.append(g)
         g.active_is_multidimensional = True
         for index in m.axes_manager:
-            m.fit()
+            m.fit(fitter='leastsq')
         self.model = m
 
     def test_model_signal_indexer_slice(self):

hyperspy/tests/model/test_fit_component.py

@@ -44,15 +44,15 @@ def test_fit_component(self):
 
         g1 = Gaussian()
         m.append(g1)
-        m.fit_component(g1, signal_range=(4000, 6000))
+        m.fit_component(g1, signal_range=(4000, 6000), fitter='leastsq')
         np.testing.assert_allclose(self.g.function(axis),
                                    g1.function(axis),
                                    rtol=self.rtol,
                                    atol=10e-3)
 
     def test_component_not_in_model(self):
         with pytest.raises(ValueError):
-            self.model.fit_component(self.g)
+            self.model.fit_component(self.g, fitter='leastsq')
 
 
 class TestFitSeveralComponent:
@@ -106,7 +106,7 @@ def test_fit_component_active_state(self):
         g3 = self.g3
         g2.active = True
         g3.active = False
-        m.fit_component(g1, signal_range=(4500, 5200), fit_independent=True)
+        m.fit_component(g1, signal_range=(4500, 5200), fit_independent=True, fitter='leastsq')
         np.testing.assert_allclose(self.gs1.function(axis),
                                    g1.function(axis),
                                    rtol=self.rtol,
@@ -123,7 +123,7 @@ def test_fit_component_free_state(self):
         g3 = self.g3
         g2.A.free = False
         g2.sigma.free = False
-        m.fit_component(g1, signal_range=(4500, 5200))
+        m.fit_component(g1, signal_range=(4500, 5200), fitter='leastsq')
         np.testing.assert_allclose(self.gs1.function(axis),
                                    g1.function(axis),
                                    rtol=self.rtol,
@@ -146,9 +146,9 @@ def test_fit_multiple_component(self):
         g1 = self.g1
         g2 = self.g2
         g3 = self.g3
-        m.fit_component(g1, signal_range=(4500, 5200))
-        m.fit_component(g2, signal_range=(1500, 2200))
-        m.fit_component(g3, signal_range=(5800, 6150))
+        m.fit_component(g1, signal_range=(4500, 5200), fitter='leastsq')
+        m.fit_component(g2, signal_range=(1500, 2200), fitter='leastsq')
+        m.fit_component(g3, signal_range=(5800, 6150), fitter='leastsq')
         np.testing.assert_allclose(self.model.signal.data,
                                    m(),
                                    rtol=self.rtol,
@@ -168,7 +168,7 @@ def setup_method(self, method):
     def test_fit_spectrum_image(self):
         m = self.model
         G = self.G
-        m.fit_component(G, signal_range=(2,7), only_current=False)
+        m.fit_component(G, signal_range=(2,7), only_current=False, fitter='leastsq')
         m.axes_manager.indices = (0,0)
         A = G.A.value
         m.axes_manager.indices = (1,1)