Merge remote-tracking branch 'upstream/RELEASE_next_patch' into RELEA…

…SE_next_minor

doc/user_guide/bibliography.rst

@@ -169,7 +169,7 @@ Bibliography
 :ref:`[Zhou2011] <Zhou2011>`
    T. Zhou and D. Tao, "GoDec: Randomized Low-rank
    & Sparse Matrix Decomposition in Noisy Case", *ICML-11* (2011): 33–40
-   [`<http://www.icml-2011.org/papers/41_icmlpaper.pdf>`_].
+   [`<https://icml.cc/Conferences/2011/papers/41_icmlpaper.pdf>`_].
 
 .. _Schaffer2004:
 

hyperspy/_signals/signal1d.py

@@ -16,11 +16,11 @@
 # You should have received a copy of the GNU General Public License
 # along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
 
-import os
 import logging
 import math
+import os
+import warnings
 
-import matplotlib.pyplot as plt
 import numpy as np
 import dask.array as da
 from scipy import interpolate
@@ -29,7 +29,8 @@
 
 from hyperspy.signal import BaseSignal
 from hyperspy._signals.common_signal1d import CommonSignal1D
-from hyperspy.signal_tools import SpikesRemoval, SpikesRemovalInteractive
+from hyperspy.signal_tools import (
+    SpikesRemoval, SpikesRemovalInteractive, SimpleMessage)
 from hyperspy.models.model1d import Model1D
 from hyperspy.misc.lowess_smooth import lowess
 from hyperspy.misc.utils import is_binned # remove in v2.0
@@ -270,9 +271,15 @@ def __init__(self, *args, **kwargs):
             raise ValueError("Signal1D can't be ragged.")
         super().__init__(*args, **kwargs)
 
-    def _get_spikes_diagnosis_histogram_data(self, signal_mask=None,
-                                             navigation_mask=None,
-                                             **kwargs):
+    def _spikes_diagnosis(
+            self,
+            signal_mask=None,
+            navigation_mask=None,
+            show_plot=False,
+            use_gui=False,
+            **kwargs
+            ):
+
         self._check_signal_dimension_equals_one()
         dc = self.data
         axis = self.axes_manager.signal_axes[0].axis
@@ -281,22 +288,41 @@ def _get_spikes_diagnosis_histogram_data(self, signal_mask=None,
             axis = axis[~signal_mask]
         if navigation_mask is not None:
             dc = dc[~navigation_mask, :]
+        if dc.size == 0:
+            raise ValueError("The data size must be higher than 0.")
         der = abs(np.gradient(dc, axis, axis=-1))
         n = ((~navigation_mask).sum() if navigation_mask else
              self.axes_manager.navigation_size)
 
         # arbitrary cutoff for number of spectra necessary before histogram
         # data is compressed by finding maxima of each spectrum
-        tmp = BaseSignal(der) if n < 2000 else BaseSignal(
-            np.ravel(der.max(-1)))
+        tmp = BaseSignal(der) if n < 2000 else BaseSignal(np.ravel(der.max(-1)))
+
+        s_ = tmp.get_histogram(**kwargs)
+        s_.axes_manager[0].name = "Derivative magnitude"
+        s_.metadata.Signal.quantity = "Counts"
+        s_.metadata.General.title = "Spikes Analysis"
+
+        if s_.data.size == 1:
+            message = "The derivative of the data is constant."
+            if use_gui:
+                m = SimpleMessage(text=message)
+                try:
+                    m.gui()
+                except (NotImplementedError, ImportError):
+                    # This is only available for traitsui, in case of ipywidgets
+                    # we show a warning
+                    warnings.warn(message)
+            else:
+                warnings.warn(message)
+        elif show_plot:
+            s_.plot(norm="log")
 
-        # get histogram signal using smart binning and plot
-        return tmp.get_histogram(**kwargs)
+        return s_
 
-    def spikes_diagnosis(self, signal_mask=None,
-                         navigation_mask=None,
-                         **kwargs):
-        """Plots a histogram to help in choosing the threshold for
+    def spikes_diagnosis(self, signal_mask=None, navigation_mask=None, **kwargs):
+        """
+        Plots a histogram to help in choosing the threshold for
         spikes removal.
 
         Parameters
@@ -312,27 +338,13 @@ def spikes_diagnosis(self, signal_mask=None,
         spikes_removal_tool
 
         """
-        tmph = self._get_spikes_diagnosis_histogram_data(signal_mask,
-                                                         navigation_mask,
-                                                         **kwargs)
-        tmph.plot()
-
-        # Customize plot appearance
-        plt.gca().set_title('')
-        plt.gca().fill_between(tmph.axes_manager[0].axis,
-                               tmph.data,
-                               facecolor='#fddbc7',
-                               interpolate=True,
-                               color='none')
-        ax = tmph._plot.signal_plot.ax
-        axl = tmph._plot.signal_plot.ax_lines[0]
-        axl.set_line_properties(color='#b2182b')
-        plt.xlabel('Derivative magnitude')
-        plt.ylabel('Log(Counts)')
-        ax.set_yscale('log')
-        ax.set_ylim(10 ** -1, plt.ylim()[1])
-        ax.set_xlim(plt.xlim()[0], 1.1 * plt.xlim()[1])
-        plt.draw()
+        self._spikes_diagnosis(
+            signal_mask=signal_mask,
+            navigation_mask=navigation_mask,
+            show_plot=True,
+            use_gui=False,
+            **kwargs
+            )
 
     spikes_diagnosis.__doc__ %= (SIGNAL_MASK_ARG, NAVIGATION_MASK_ARG)
 

hyperspy/drawing/_widgets/range.py

@@ -18,13 +18,18 @@
 
 import inspect
 import logging
+from packaging.version import Version
 
+import matplotlib
 import numpy as np
 
 from hyperspy.drawing.widget import ResizableDraggableWidgetBase
 from hyperspy.defaults_parser import preferences
 
-from hyperspy.external.matplotlib.widgets import SpanSelector
+if Version(matplotlib.__version__) >= Version('3.6.0'):
+    from matplotlib.widgets import SpanSelector
+else:
+    from hyperspy.external.matplotlib.widgets import SpanSelector
 
 _logger = logging.getLogger(__name__)
 

hyperspy/signal_tools.py

@@ -1757,10 +1757,13 @@ def __init__(self, signal, navigation_mask=None, signal_mask=None,
             signal.axes_manager.indices = self.coordinates[0]
         if threshold == 'auto':
             # Find the first zero of the spikes diagnosis plot
-            hist = signal._get_spikes_diagnosis_histogram_data(
+            hist = signal._spikes_diagnosis(
                     signal_mask=signal_mask,
                     navigation_mask=navigation_mask,
-                    max_num_bins=max_num_bins)
+                    max_num_bins=max_num_bins,
+                    show_plot=False,
+                    use_gui=False,
+                    )
             zero_index = np.where(hist.data == 0)[0]
             if zero_index.shape[0] > 0:
                 index = zero_index[0]
@@ -1950,9 +1953,13 @@ def _click_to_show_instructions_fired(self):
                         title="Instructions"),
 
     def _show_derivative_histogram_fired(self):
-        self.signal.spikes_diagnosis(signal_mask=self.signal_mask,
-                                     navigation_mask=self.navigation_mask,
-                                     max_num_bins=self.max_num_bins)
+        self.signal._spikes_diagnosis(
+            signal_mask=self.signal_mask,
+            navigation_mask=self.navigation_mask,
+            max_num_bins=self.max_num_bins,
+            show_plot=True,
+            use_gui=True,
+            )
 
     def _reset_line(self):
         if self.interpolated_line is not None:

hyperspy/tests/signals/test_eels.py

@@ -260,18 +260,22 @@ def test_spikes_diagnosis(self):
         self.signal.spikes_diagnosis(zero_loss_peak_mask_width=5.0)
 
         zlp_mask = self.signal.get_zero_loss_peak_mask()
-        hist_data = self.signal._get_spikes_diagnosis_histogram_data(
-            signal_mask=zlp_mask, bins=25)
+        hist_data = self.signal._spikes_diagnosis(signal_mask=zlp_mask, bins=25)
         expected_data = np.zeros(25)
         expected_data[0] = 232
         expected_data[12] = 1
         expected_data[-1] = 1
         np.testing.assert_allclose(hist_data.data, expected_data)
 
-        hist_data2 = self.signal._get_spikes_diagnosis_histogram_data(bins=25)
+        hist_data2 = self.signal._spikes_diagnosis(bins=25)
         expected_data2 = np.array([285, 11, 13, 0, 0, 1, 12,  0])
         np.testing.assert_allclose(hist_data2.data[:8], expected_data2)
 
+        # mask all to check that it raises an error when there is no data
+        signal_mask = self.signal.inav[0,1].data.astype(bool)
+        with pytest.raises(ValueError):
+            self.signal.spikes_diagnosis(signal_mask=signal_mask)
+
 
 def test_spikes_removal_tool_no_zlp():
     s = hs.datasets.artificial_data.get_core_loss_eels_line_scan_signal()
@@ -280,6 +284,23 @@ def test_spikes_removal_tool_no_zlp():
         s.spikes_removal_tool(zero_loss_peak_mask_width=5.0)
 
 
+def test_spikes_diagnosis_constant_derivative():
+    s = hs.signals.Signal1D(np.arange(20).reshape(2, 10))
+    with pytest.warns():
+        s._spikes_diagnosis(use_gui=False)
+
+    hs.preferences.GUIs.enable_traitsui_gui = False
+    with pytest.warns():
+        s._spikes_diagnosis(use_gui=True)
+
+    hs.preferences.GUIs.enable_traitsui_gui = True
+    try:
+        import hyperspy_gui_traitsui
+        s._spikes_diagnosis(use_gui=True)
+    except ImportError:
+        pass
+
+
 @lazifyTestClass
 class TestPowerLawExtrapolation:
 

hyperspy/tests/signals/test_spike_removal_tool.py

@@ -32,6 +32,7 @@ def test_spikes_removal_tool():
     s.data[1, 2, 14] += 1
 
     sr = SpikesRemovalInteractive(s)
+    sr._show_derivative_histogram_fired()
     sr.threshold = 1.5
     sr.find()
     assert s.axes_manager.indices == (0, 1)
@@ -60,17 +61,13 @@ def test_spikes_removal_tool():
     assert s.axes_manager.indices == (0, 0)
 
 
-add_noise_params = [
-    [False, 5],
-    [True, 1]
-]
-
-
 def test_spikes_removal_tool_navigation_dimension_0():
     #Artificial Signal
     s = Signal1D(np.ones(1234))
     #Add a spike
-    s.data[333] = 666
+    s.data[333] = 5
+    np.random.seed(1)
+    s.add_gaussian_noise(0.01)
 
     assert s.axes_manager.navigation_dimension == 0
 
@@ -81,7 +78,7 @@ def test_spikes_removal_tool_navigation_dimension_0():
 
     sr.apply()
 
-    np.testing.assert_allclose(s.data[333], 1, atol=1e-4)
+    np.testing.assert_allclose(s.data[333], 1, atol=0.02)
 
 
 @pytest.mark.parametrize(("add_noise, decimal"), [(True, 1), (False, 5)])
@@ -115,8 +112,8 @@ def test_spikes_removal_tool_non_interactive_masking():
     navigation_mask[1, 0] = True
     signal_mask = np.zeros((30,), dtype='bool')
     signal_mask[28:] = True
-    sr = s.spikes_removal_tool(threshold=0.5, interactive=False, add_noise=False,
-                               navigation_mask=navigation_mask, signal_mask=signal_mask)
+    s.spikes_removal_tool(threshold=0.5, interactive=False, add_noise=False,
+                          navigation_mask=navigation_mask, signal_mask=signal_mask)
     np.testing.assert_almost_equal(s.data[1, 0, 1], 3, decimal=5)
     np.testing.assert_almost_equal(s.data[0, 2, 29], 2, decimal=5)
     np.testing.assert_almost_equal(s.data[1, 2, 14], 1, decimal=5)

upcoming_changes/3005.bugfix.rst

@@ -0,0 +1 @@
+Fix handling constant derivative in :py:meth:`~._signals.signal1D.Signal1D.spikes_removal_tool`

upcoming_changes/3015.maintenance.rst

@@ -0,0 +1 @@
+Fix hyperlink in bibliography

upcoming_changes/3016.maintenance.rst

@@ -0,0 +1 @@
+Fix matplotlib ``SpanSelector`` import for matplotlib 3.6