Add all .asd frames as images to img_dict in io.py

asd_file_format.ipynb

@@ -399,6 +399,8 @@
     "        y = read_int32(f)\n",
     "        coords_blue.append((x, y))\n",
     "\n",
+    "    return header_dict\n",
+    "\n",
     "\n",
     "class VoltageLevelConverter:\n",
     "    def __init__(self, analogue_digital_range, max_voltage, scaling_factor, resolution):\n",
@@ -556,9 +558,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def load_asd():\n",
-    "    channel = \"TP\"\n",
-    "\n",
+    "def load_asd(channel: str):\n",
     "    with open(FILE_PATH, \"rb\") as f:\n",
     "        file_version = read_file_version(f)\n",
     "\n",

topostats/io.py

@@ -919,45 +919,52 @@ def get_data(self) -> None:
                     else:
                         raise
                 else:
-                    self._check_image_size_and_add_to_dict()
+                    if suffix == ".asd":
+                        for index, frame in enumerate(self.image):
+                            self._check_image_size_and_add_to_dict(image=frame, filename=f"{self.filename}_{index}")
+                    else:
+                        self._check_image_size_and_add_to_dict(image=self.image, filename=self.filename)
             else:
                 raise ValueError(
                     f"File type {suffix} not yet supported. Please make an issue at \
                 https://github.com/AFM-SPM/TopoStats/issues, or email [email protected] to request support for \
                 this file type."
                 )
 
-    def _check_image_size_and_add_to_dict(self) -> None:
+    def _check_image_size_and_add_to_dict(self, image: np.ndarray, filename: str) -> None:
         """Check the image is above a minimum size in both dimensions.
 
         Images that do not meet the minimum size are not included for processing.
+
+        Parameters
+        ----------
+        image: np.ndarray
+            An array of the extracted AFM image.
+        filename: str
+            The name of the file
         """
-        if self.image.shape[0] < self.MINIMUM_IMAGE_SIZE or self.image.shape[1] < self.MINIMUM_IMAGE_SIZE:
-            LOGGER.warning(f"[{self.filename}] Skipping, image too small: {self.image.shape}")
+        if image.shape[0] < self.MINIMUM_IMAGE_SIZE or image.shape[1] < self.MINIMUM_IMAGE_SIZE:
+            LOGGER.warning(f"[{filename}] Skipping, image too small: {image.shape}")
         else:
-            self.add_to_dict()
-            LOGGER.info(f"[{self.filename}] Image added to processing.")
+            self.add_to_dict(image=image, filename=filename)
+            LOGGER.info(f"[{filename}] Image added to processing.")
 
-    def add_to_dict(self) -> None:
+    def add_to_dict(self, image: np.ndarray, filename: str) -> None:
         """Adds the image, image path and pixel to nanometre scaling value to the img_dic dictionary under
         the key filename.
 
         Parameters
         ----------
-        filename: str
-            The filename, idealy without an extension.
         image: np.ndarray
             An array of the extracted AFM image.
-        img_path: str
-            The path to the AFM file (with a frame number if applicable)
-        px_2_nm: float
-            The length of a pixel in nm.
+        filename: str
+            The name of the file
         """
-        self.img_dict[self.filename] = {
-            "filename": self.filename,
-            "img_path": self.img_path.with_name(self.filename),
+        self.img_dict[filename] = {
+            "filename": filename,
+            "img_path": self.img_path.with_name(filename),
             "pixel_to_nm_scaling": self.pixel_to_nm_scaling,
-            "image_original": self.image,
+            "image_original": image,
             "image_flattened": None,
             "grain_masks": self.grain_masks,
         }