Fix wrong renames

hyperspy/_signals/signal1d.py

@@ -1302,7 +1302,7 @@ def hanning_taper(self, side='both', channels=None, offset=0):
         # TODO: generalize it
         self._check_signal_dimension_equals_one()
         if channels is None:
-            channels = int(round(len(self()) * 0.02))
+            channels = int(round(len(self._get_current_data()) * 0.02))
             if channels < 20:
                 channels = 20
         dc = self._data_aligned_with_axes

hyperspy/signal_tools.py

@@ -635,15 +635,15 @@ def _differential_order_changed(self, old, new):
         super()._differential_order_changed(old, new)
 
     def diff_model2plot(self, axes_manager=None):
-        self.single_spectrum.data = self._get_current_data().copy()
+        self.single_spectrum.data = self.signal._get_current_data().copy()
         self.single_spectrum.smooth_savitzky_golay(
             polynomial_order=self.polynomial_order,
             window_length=self.window_length,
             differential_order=self.differential_order)
         return self.single_spectrum.data
 
     def model2plot(self, axes_manager=None):
-        self.single_spectrum.data = self._get_current_data().copy()
+        self.single_spectrum.data = self.signal._get_current_data().copy()
         self.single_spectrum.smooth_savitzky_golay(
             polynomial_order=self.polynomial_order,
             window_length=self.window_length,
@@ -680,7 +680,7 @@ def _number_of_iterations_changed(self, old, new):
         self.update_lines()
 
     def model2plot(self, axes_manager=None):
-        self.single_spectrum.data = self._get_current_data().copy()
+        self.single_spectrum.data = self.signal._get_current_data().copy()
         self.single_spectrum.smooth_lowess(
             smoothing_parameter=self.smoothing_parameter,
             number_of_iterations=self.number_of_iterations,
@@ -703,7 +703,7 @@ def _smoothing_parameter_changed(self, old, new):
         self.update_lines()
 
     def model2plot(self, axes_manager=None):
-        self.single_spectrum.data = self._get_current_data().copy()
+        self.single_spectrum.data = self.signal._get_current_data().copy()
         self.single_spectrum.smooth_tv(
             smoothing_parameter=self.smoothing_parameter,
             show_progressbar=False)
@@ -734,7 +734,7 @@ def _order_changed(self, old, new):
     def model2plot(self, axes_manager=None):
         b, a = sp_signal.butter(self.order, self.cutoff_frequency_ratio,
                                 self.type)
-        smoothed = sp_signal.filtfilt(b, a, self._get_current_data())
+        smoothed = sp_signal.filtfilt(b, a, self.signal._get_current_data())
         return smoothed
 
     def apply(self):
@@ -1340,7 +1340,7 @@ def bg_to_plot(self, axes_manager=None, fill_with=np.nan):
         return to_return
 
     def rm_to_plot(self, axes_manager=None, fill_with=np.nan):
-        return self._get_current_data() - self.bg_line.line.get_ydata()
+        return self.signal._get_current_data() - self.bg_line.line.get_ydata()
 
     def span_selector_changed(self, *args, **kwargs):
         super().span_selector_changed()
@@ -1545,7 +1545,7 @@ def __init__(self, signal, navigation_mask=None, signal_mask=None,
         self.argmax = None
         self.derivmax = None
         self.spline_order = 1
-        self._temp_mask = np.zeros(self._get_current_data().shape, dtype='bool')
+        self._temp_mask = np.zeros(self.signal._get_current_data().shape, dtype='bool')
         self.index = 0
         self.threshold = threshold
         md = self.signal.metadata
@@ -1566,7 +1566,7 @@ def __init__(self, signal, navigation_mask=None, signal_mask=None,
 
     def detect_spike(self):
         axis = self.signal.axes_manager.signal_axes[-1].axis
-        derivative = np.gradient(self._get_current_data(), axis)
+        derivative = np.gradient(self.signal._get_current_data(), axis)
         if self.signal_mask is not None:
             derivative[self.signal_mask] = 0
         if self.argmax is not None:
@@ -1620,7 +1620,7 @@ def get_interpolation_range(self):
         return left, right
 
     def get_interpolated_spectrum(self, axes_manager=None):
-        data = self._get_current_data().copy()
+        data = self.signal._get_current_data().copy()
         axis = self.signal.axes_manager.signal_axes[0]
         left, right = self.get_interpolation_range()
         pad = self.spline_order
@@ -1670,7 +1670,7 @@ def get_interpolated_spectrum(self, axes_manager=None):
     def remove_all_spikes(self):
         spike = self.find()
         while spike:
-            self._get_current_data()[:] = self.get_interpolated_spectrum()
+            self.signal._get_current_data()[:] = self.get_interpolated_spectrum()
             spike = self.find()
 
 
@@ -1750,7 +1750,7 @@ def find(self, back=False):
             return
         else:
             minimum = max(0, self.argmax - 50)
-            maximum = min(len(self._get_current_data()) - 1, self.argmax + 50)
+            maximum = min(len(self.signal._get_current_data()) - 1, self.argmax + 50)
             thresh_label = DerivativeTextParameters(
                 text=r"$\mathsf{\delta}_\mathsf{max}=$",
                 color="black")
@@ -1784,7 +1784,7 @@ def update_signal_mask(self):
             self.mask_filling.remove()
         if self.signal_mask is not None:
             self.mask_filling = self.ax.fill_between(self.axis.axis,
-                                                     self._get_current_data(), 0,
+                                                     self.signal._get_current_data(), 0,
                                                      where=self.signal_mask,
                                                      facecolor='blue',
                                                      alpha=0.5)
@@ -1829,7 +1829,7 @@ def span_selector_changed(self, *args, **kwargs):
     def apply(self):
         if not self.interpolated_line:  # No spike selected
             return
-        self._get_current_data()[:] = self.get_interpolated_spectrum()
+        self.signal._get_current_data()[:] = self.get_interpolated_spectrum()
         self.signal.events.data_changed.trigger(obj=self.signal)
         self.update_spectrum_line()
         self.interpolated_line.close()

hyperspy/tests/model/test_linear_model.py

@@ -89,7 +89,7 @@ def test_gaussian(self, weighted):
             m.multifit(optimizer='lstsq')
         multi = m.as_signal()
 
-        np.testing.assert_allclose(single(), multi())
+        np.testing.assert_allclose(single._get_current_data(), multi._get_current_data())
 
     def test_map_values_std_isset(self, weighted):
         self._post_setup_method(weighted)
@@ -133,7 +133,7 @@ def test_offset(self, weighted):
             m.multifit(optimizer='lstsq')
         multi = m.as_signal()
         # compare fits from first pixel
-        np.testing.assert_allclose(single(), multi())
+        np.testing.assert_allclose(single._get_current_data(), multi._get_current_data())
 
     def test_channel_switches(self, weighted):
         self._post_setup_method(weighted)
@@ -152,11 +152,11 @@ def test_channel_switches(self, weighted):
            m.multifit(optimizer='lstsq')
         multi = m.as_signal()
 
-        np.testing.assert_allclose(single(), multi())
+        np.testing.assert_allclose(single._get_current_data(), multi._get_current_data())
 
         m.fit()
         single_nonlinear = m.as_signal()
-        np.testing.assert_allclose(single(), single_nonlinear())
+        np.testing.assert_allclose(single._get_current_data(), single_nonlinear._get_current_data())
 
     def test_multifit_ridge(self, weighted):
         pytest.importorskip("sklearn")

hyperspy/tests/model/test_model.py

@@ -154,15 +154,15 @@ def test_model2plot_own_am(self):
         np.testing.assert_array_equal(
             res, np.array([np.nan, 0.5, 0.25, np.nan, np.nan])
         )
-        assert m._call__.called
+        assert m.__call__.called
         assert m._call__.call_args[1] == {"non_convolved": False, "onlyactive": True}
         assert not m.fetch_stored_values.called
 
     def test_model2plot_other_am(self):
         m = self.model
         res = m._model2plot(m.axes_manager.deepcopy(), out_of_range2nans=False)
         np.testing.assert_array_equal(res, np.array([0.5, 0.25]))
-        assert m._call__.called
+        assert m.__call__.called
         assert m._call__.call_args[1] == {"non_convolved": False, "onlyactive": True}
         assert 2 == m.fetch_stored_values.call_count