notebooks_mod3

notebooks/spatialdata_tutorials/7_image_params.ipynb

@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -137,6 +137,29 @@
     "\n",
     "# Example: Assuming `data_array` is your xArray DataArray with dims (channels, height, width)\n",
     "def process_xarray(data_array, sigma=5, downsize_factor=0.1):\n",
+    "    \"\"\"\n",
+    "    Processes a 3D xarray by applying Gaussian smoothing and downscaling on each 2D slice.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - data_array: xarray.DataArray\n",
+    "        A 3D xarray where the first dimension represents slices, and the next two dimensions\n",
+    "        represent 2D spatial data.\n",
+    "    - sigma: float, optional (default=5)\n",
+    "        The standard deviation for the Gaussian kernel used in smoothing.\n",
+    "    - downsize_factor: float, optional (default=0.1)\n",
+    "        The factor by which each 2D slice is resized during downscaling.\n",
+    "\n",
+    "    Returns:\n",
+    "    - smoothed_and_downsized: numpy.ndarray\n",
+    "        A 3D numpy array with processed slices, where the first dimension corresponds to\n",
+    "        the original slicing dimension, and the remaining dimensions are the downsized\n",
+    "        2D spatial data.\n",
+    "\n",
+    "    Notes:\n",
+    "    - The function loops through each slice of the input data, applies Gaussian smoothing,\n",
+    "      downsizes the smoothed array, and stacks the results back into a single 3D array.\n",
+    "    - Ensure that `data_array` has the correct shape and data types for processing.\n",
+    "    \"\"\"\n",
     "    processed_slices = []\n",
     "    \n",
     "    for i in range(data_array.shape[0]):  # Loop over the first dimension\n",
@@ -150,11 +173,10 @@
     "        \n",
     "        processed_slices.append(downscaled)\n",
     "    \n",
-    "    smoothed_and_downsized=np.stack(processed_slices , axis=0)\n",
-    "    # Stack back into an xArray\n",
-    "    \n",
+    "    smoothed_and_downsized = np.stack(processed_slices, axis=0)\n",
     "    return smoothed_and_downsized\n",
     "\n",
+    "\n",
     "# Example usage\n",
     "# Assuming your xArray is named `image_array`\n",
     "smoothed_and_downsized = process_xarray(imarray, sigma=5, downsize_factor=0.5)\n",