Fix #1045. Ensure line plot displays integer-valued data items in log…

… display.

nion/swift/LineGraphCanvasItem.py

@@ -75,7 +75,8 @@ def calculate_y_axis(xdata_list: typing.Sequence[typing.Optional[DataAndMetadata
         calibrated_data_min = None
         for xdata in xdata_list:
             if xdata and xdata.data_shape[-1] > 0:
-                uncalibrated_data = xdata.data
+                # force the uncalibrated_data to be float so that numpy.amin with a numpy.inf initial value works.
+                uncalibrated_data = xdata.data if numpy.issubdtype(xdata.data.dtype, numpy.floating) else xdata.data.astype(float)
                 if uncalibrated_data is not None:
                     if data_style == "log":
                         calibrated_origin = xdata.intensity_calibration.convert_from_calibrated_value(0.0)
@@ -96,7 +97,8 @@ def calculate_y_axis(xdata_list: typing.Sequence[typing.Optional[DataAndMetadata
         calibrated_data_max = None
         for xdata in xdata_list:
             if xdata and xdata.data_shape[-1] > 0:
-                uncalibrated_data = xdata.data
+                # force the uncalibrated_data to be float so that numpy.amin with a numpy.inf initial value works.
+                uncalibrated_data = xdata.data if numpy.issubdtype(xdata.data.dtype, numpy.floating) else xdata.data.astype(float)
                 if uncalibrated_data is not None:
                     if data_style == "log":
                         calibrated_origin = xdata.intensity_calibration.convert_from_calibrated_value(0.0)

nion/swift/resources/changes.json

@@ -2,6 +2,10 @@
   {
     "version": "UNRELEASED",
     "notes": [
+      {
+        "issues": ["https://github.com/nion-software/nionswift/issues/1045"],
+        "summary": "Fix issue when displaying line plot of integer-valued 1D data item using log scale."
+      },
       {
         "issues": ["https://github.com/nion-software/nionswift/issues/592"],
         "summary": "Importing valid data always succeeds by assign new identifiers, instead of silently skipping possible duplicates."

nion/swift/test/LineGraphCanvasItem_test.py

@@ -141,6 +141,20 @@ def test_display_limits_are_reasonable_when_using_calibrated_log_scale(self):
                 self.assertAlmostEqual(numpy.nanmin(calibrated_data), math.log10(numpy.nanmin(data)))
                 self.assertAlmostEqual(numpy.nanmax(calibrated_data), math.log10(numpy.nanmax(data)))
 
+    def test_line_plot_with_log_scale_displays_integers(self):
+        data = numpy.array([1,2,3], dtype=int)
+        data_style = "log"
+        xdata = DataAndMetadata.new_data_and_metadata(data)
+        # at some point, calculate_y_axis failed to return without exception when the data type was int.
+        # so the main point of this test is to ensure that the function returns without exception.
+        calibrated_data_min, calibrated_data_max, y_ticker = LineGraphCanvasItem.calculate_y_axis([xdata], None, None, data_style)
+        # the asserts below are a sanity check to ensure that the function is returning reasonable values.
+        # note: these are calibrated values for the AXES not the original data. furthermore they will be log10.
+        # so checking that the first value is negative (original axes value is less than 1), and the second value
+        # is positive (original axes value is greater than 1) is a reasonable check.
+        self.assertLess(calibrated_data_min, 0.0)
+        self.assertGreater(calibrated_data_max, 0.0)
+
     def test_graph_segments_are_calculated_correctly_with_nans(self):
         # this was a bug in the original implementation
         data = numpy.zeros((16,))