Move shutil import to imports cell to fix PEP8

Signed-off-by: Vidya Sagar <vidyasagarreddy.venna@gmail.com>

Author: ViSaReVe

reconstruction/MRI_reconstruction/tutorials/01_kspace_basics_fastmri_knee.ipynb

@@ -84,80 +84,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "GurF5gp1EUEA",
     "outputId": "3df392a3-2936-494b-ec16-f96038c5b9ca"
    },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "MONAI version: 1.6.dev2607\n",
-      "Numpy version: 2.0.2\n",
-      "Pytorch version: 2.9.0+cpu\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 02a5644c9062d8c81f1ab94808d4f8cfa7f28a3f\n",
-      "MONAI __file__: /usr/local/lib/python3.12/dist-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "Nibabel version: 5.3.3\n",
-      "scikit-image version: 0.25.2\n",
-      "scipy version: 1.16.3\n",
-      "Pillow version: 11.3.0\n",
-      "Tensorboard version: 2.19.0\n",
-      "gdown version: 5.2.1\n",
-      "TorchVision version: 0.24.0+cpu\n",
-      "tqdm version: 4.67.3\n",
-      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "psutil version: 5.9.5\n",
-      "pandas version: 2.2.2\n",
-      "einops version: 0.8.2\n",
-      "transformers version: 5.0.0\n",
-      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://monai.readthedocs.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "import h5py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "from monai.apps.reconstruction.fastmri_reader import FastMRIReader\n",
-    "from monai.apps.reconstruction.transforms.dictionary import (\n",
-    "    EquispacedKspaceMaskd,\n",
-    "    RandomKspaceMaskd,\n",
-    "    ReferenceBasedNormalizeIntensityd,\n",
-    ")\n",
-    "from monai.config import print_config\n",
-    "from monai.transforms import (\n",
-    "    CenterSpatialCropd,\n",
-    "    Compose,\n",
-    "    EnsureChannelFirstd,\n",
-    "    EnsureTyped,\n",
-    "    Lambdad,\n",
-    "    LoadImaged,\n",
-    "    ThresholdIntensityd,\n",
-    ")\n",
-    "from monai.utils.type_conversion import convert_data_type\n",
-    "\n",
-    "print_config()"
-   ]
+   "outputs": [],
+   "source": "import os\nimport shutil\n\nimport h5py\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\nfrom monai.apps.reconstruction.fastmri_reader import FastMRIReader\nfrom monai.apps.reconstruction.transforms.dictionary import (\n    EquispacedKspaceMaskd,\n    RandomKspaceMaskd,\n    ReferenceBasedNormalizeIntensityd,\n)\nfrom monai.config import print_config\nfrom monai.transforms import (\n    CenterSpatialCropd,\n    Compose,\n    EnsureChannelFirstd,\n    EnsureTyped,\n    Lambdad,\n    LoadImaged,\n    ThresholdIntensityd,\n)\nfrom monai.utils.type_conversion import convert_data_type\n\nprint_config()"
   },
   {
    "cell_type": "markdown",
@@ -188,58 +124,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "TQOy8nA0EUEA",
     "outputId": "98b4e840-035f-4e47-ed35-777a21c3ed2d"
    },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Mounted at /content/drive\n",
-      "Copied file1000000.h5 to /content/fastmri_data/knee_singlecoil_val\n",
-      "Using sample file: /content/fastmri_data/knee_singlecoil_val/file1000000.h5\n",
-      "Total .h5 files found: 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Update this path to where your fastMRI knee single-coil data is stored.\n",
-    "# You only need ONE .h5 file from the knee_singlecoil_val set.\n",
-    "\n",
-    "# --- Google Colab: mount Drive and copy data ---\n",
-    "import shutil\n",
-    "\n",
-    "from google.colab import drive\n",
-    "\n",
-    "drive.mount(\"/content/drive\")\n",
-    "\n",
-    "drive_folder = \"/content/drive/MyDrive/fastmri_data\"\n",
-    "data_path = \"/content/fastmri_data/knee_singlecoil_val\"\n",
-    "os.makedirs(data_path, exist_ok=True)\n",
-    "\n",
-    "files = [f for f in os.listdir(drive_folder) if f.endswith(\".h5\")]\n",
-    "if files:\n",
-    "    shutil.copy(os.path.join(drive_folder, files[0]), data_path)\n",
-    "    print(f\"Copied {files[0]} to {data_path}\")\n",
-    "else:\n",
-    "    raise FileNotFoundError(\n",
-    "        f\"No .h5 files found in {drive_folder}\\n\" \"Please upload at least one .h5 file from knee_singlecoil_val.\"\n",
-    "    )\n",
-    "\n",
-    "# --- Local users: comment out the Colab block above and set your path ---\n",
-    "# data_path = os.path.join(\"YOUR_DIR_HERE\", \"knee_singlecoil_val\")\n",
-    "\n",
-    "sample_files = sorted([f for f in os.listdir(data_path) if f.endswith(\".h5\")])\n",
-    "sample_file = os.path.join(data_path, sample_files[0])\n",
-    "print(f\"Using sample file: {sample_file}\")\n",
-    "print(f\"Total .h5 files found: {len(sample_files)}\")"
-   ]
+   "outputs": [],
+   "source": "# Update this path to where your fastMRI knee single-coil data is stored.\n# You only need ONE .h5 file from the knee_singlecoil_val set.\n\n# --- Google Colab: mount Drive and copy data ---\nfrom google.colab import drive\n\ndrive.mount(\"/content/drive\")\n\ndrive_folder = \"/content/drive/MyDrive/fastmri_data\"\ndata_path = \"/content/fastmri_data/knee_singlecoil_val\"\nos.makedirs(data_path, exist_ok=True)\n\nfiles = [f for f in os.listdir(drive_folder) if f.endswith(\".h5\")]\nif files:\n    shutil.copy(os.path.join(drive_folder, files[0]), data_path)\n    print(f\"Copied {files[0]} to {data_path}\")\nelse:\n    raise FileNotFoundError(\n        f\"No .h5 files found in {drive_folder}\\n\"\n        \"Please upload at least one .h5 file from knee_singlecoil_val.\"\n    )\n\n# --- Local users: comment out the Colab block above and set your path ---\n# data_path = os.path.join(\"YOUR_DIR_HERE\", \"knee_singlecoil_val\")\n\nsample_files = sorted([f for f in os.listdir(data_path) if f.endswith(\".h5\")])\nsample_file = os.path.join(data_path, sample_files[0])\nprint(f\"Using sample file: {sample_file}\")\nprint(f\"Total .h5 files found: {len(sample_files)}\")"
   },
   {
    "cell_type": "markdown",
@@ -1038,4 +932,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}