Testing:Expand Test coverage

rsciio/dm5/_api.py

@@ -337,9 +337,6 @@ def __init__(self, image_group, tags=None, unique_id=None, file=None):
         self.unique_id = image_group["UniqueID"]
         self.file = file
 
-    def __str__(self):
-        return f"Image: {self.image_data['Data'].shape}"
-
     @property
     def ndim(self):
         return len(self.image_data["Data"].shape)
@@ -380,9 +377,6 @@ def signal_dimensions(self):
                     "Meta Data.Format attribute in the ImageTags group."
                 )
 
-    def navigation_dimensions(self):
-        return self.ndim - self.signal_dimensions()
-
     def get_axis_dict(self, axis):
         """
         Get the calibration data for a given axis.
@@ -463,9 +457,13 @@ def update_dimension(self, axis, length=None):
         """
         Update the dimension of the image for a given axis.
 
-        This is two places in the DM5 file???
-
-        Under Calibrations and under Dimension. I think that only the Calibrations should be updated.
+        Parameters
+        ----------
+        axis : int
+            The axis to update the dimension for (Starting from 0 in array order). This will be reversed to match DM's
+            axis order.
+        length : int, optional
+            The length of the axis.
 
         """
         axis = self.ndim - axis - 1
@@ -485,6 +483,12 @@ def _get_dimension(self, axis):
     def get_data(self, lazy=False):
         """
         Get the image data.
+
+        Parameters
+        ----------
+        lazy : bool, optional
+            Whether to return a dask array or a numpy
+
         """
         if lazy:
             return da.from_array(self.image_data["Data"])
@@ -494,6 +498,11 @@ def get_data(self, lazy=False):
     def update_data(self, data):
         """
         Update the image data.
+
+        Parameters
+        ----------
+        data : np.ndarray or da.Array
+            The new image data.
         """
         HyperspyWriter.overwrite_dataset(self.image_data, data, "Data")
         self.image_data.attrs.update(
@@ -522,7 +531,7 @@ def get_metadata(self):
         if "Microscope Info" in original_metadata:
             metadata["Acquisition_instrument"] = {}
             metadata["Acquisition_instrument"]["TEM"] = {}
-            metadata["Acquisition_instrument"]["TEM"]["beam_energy "] = (
+            metadata["Acquisition_instrument"]["TEM"]["beam_energy"] = (
                 original_metadata["Microscope Info"].get("Voltage", 0) / 1000
             )
             metadata["Acquisition_instrument"]["TEM"]["acquisition_mode"] = (
@@ -534,18 +543,28 @@ def get_metadata(self):
             metadata["Acquisition_instrument"]["TEM"]["camera_length"] = (
                 original_metadata["Microscope Info"].get("STEM Camera Length", 0)
             )
-            metadata["Acquisition_instrument"] = original_metadata["Microscope Info"]
-        metadata["Acquisition_instrument"] = {}
         return metadata, original_metadata
 
     def update_metadata(
         self, metadata=None, signal_dimensions=None, navigation_dimensions=None
     ):
         """
         Update the metadata for the image.
+
+        Parameters
+        ----------
+        metadata : dict, optional
+            The metadata to update.
+        signal_dimensions : int, optional
+            The number of signal dimensions.
+        navigation_dimensions : int, optional
+            The number of navigation dimensions.
         """
         if metadata is None:
             metadata = {}
+        if navigation_dimensions is None and signal_dimensions is None:
+            signal_dimensions = self.ndim
+            navigation_dimensions = 0
         formatted_metadata = {}
 
         formatted_metadata["Acquisition"] = {}
@@ -569,26 +588,35 @@ def update_metadata(
         if navigation_dimensions > 0:
             formatted_metadata["Meta Data"]["IsSequence"] = "true"
         dict2group(formatted_metadata, self.image_tags)
+
+        # Update Microscope Info
+        if (
+            "Acquisition_instrument" in metadata
+            and "TEM" in metadata["Acquisition_instrument"]
+        ):
+            self.image_tags.create_group("Microscope Info")
+            microscope_info_dict = {
+                "Voltage": metadata["Acquisition_instrument"]["TEM"].get(
+                    "beam_energy", 0
+                )
+                * 1000,
+                "Illumination Mode": metadata["Acquisition_instrument"]["TEM"].get(
+                    "acquisition_mode", "Unknown"
+                ),
+                "Indicated Magnification": metadata["Acquisition_instrument"][
+                    "TEM"
+                ].get("magnification", 0),
+                "STEM Camera Length": metadata["Acquisition_instrument"]["TEM"].get(
+                    "camera_length", 0
+                ),
+            }
+
+            dict2group(microscope_info_dict, self.image_tags["Microscope Info"])
+
         self.image_tags.create_group("UserTags")
         dict2group(metadata, self.image_tags["UserTags"])
         return
 
-    def to_signal_dict(self):
-        """
-        Convert the image to a Hyperspy signal dictionary.
-        """
-        data = self.get_data()
-        metadata, original_metadata = self.get_metadata()
-        axes = []
-        for axis in range(len(data.shape)):
-            axes.append(self.get_axis_dict(axis))
-        return {
-            "data": data,
-            "metadata": metadata,
-            "original_metadata": original_metadata,
-            "axes": axes,
-        }
-
 
 def dict2group(dictionary, group):
     for key, value in dictionary.items():

rsciio/tests/test_dm5.py

@@ -24,6 +24,7 @@
 
 from pathlib import Path
 
+import dask.array as da
 import numpy as np
 import pytest
 
@@ -83,3 +84,43 @@ def test_save_load_files(
             assert "nm" in s.axes_manager[i].units
             assert s.axes_manager[i].scale == 0.1
             assert s.axes_manager[i].size == int(original[i][-2:])
+
+    def test_save_load_undefined_axes(self, tmp_path):
+        fname = tmp_path / "test_save_undefined.dm5"
+
+        data_shape = [10, 11, 12, 13]
+        data = np.ones(data_shape, dtype=np.float32)
+        signal = hs.signals.Signal2D(data)
+        signal.save(fname, overwrite=True)
+        s = hs.load(fname)
+        for i in range(4):
+            assert s.axes_manager[i].name == ""
+            assert s.axes_manager[i].units == ""
+
+    def test_save_load_metadata(self, tmp_path):
+        fname = tmp_path / "test_save_undefined.dm5"
+
+        data_shape = [10, 11, 12, 13]
+        data = np.ones(data_shape, dtype=np.float32)
+        signal = hs.signals.Signal2D(data)
+        signal.metadata.General.title = "test"
+        signal.metadata.add_node("Acquisition_instrument.TEM")
+        signal.metadata.Acquisition_instrument.TEM.beam_energy = 200
+        signal.metadata.Acquisition_instrument.TEM.magnification = 100
+        signal.metadata.Acquisition_instrument.TEM.camera_length = 10
+
+        signal.save(fname, overwrite=True)
+        s = hs.load(fname)
+        assert s.metadata.Acquisition_instrument.TEM.beam_energy == 200
+        assert s.metadata.Acquisition_instrument.TEM.camera_length == 10
+        assert s.metadata.Acquisition_instrument.TEM.magnification == 100
+
+    def test_save_load_lazy(self, tmp_path):
+        fname = tmp_path / "test_save_lazy.dm5"
+
+        data_shape = [10, 11, 12, 13]
+        data = np.ones(data_shape, dtype=np.float32)
+        signal = hs.signals.Signal2D(data)
+        signal.save(fname, overwrite=True)
+        s = hs.load(fname, lazy=True)
+        assert isinstance(s.data, da.Array)