Mat-to-Nwb.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJSWVDRbCkMD",
        "outputId": "f60eafc3-84b5-4c37-e711-bf2378115d73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ndx-pose in /root/anaconda3/lib/python3.12/site-packages (0.1.1)\n",
            "Requirement already satisfied: pynwb<3,>=1.5.0 in /root/anaconda3/lib/python3.12/site-packages (from ndx-pose) (2.8.1)\n",
            "Requirement already satisfied: hdmf<4,>=2.5.6 in /root/anaconda3/lib/python3.12/site-packages (from ndx-pose) (3.14.2)\n",
            "Requirement already satisfied: h5py>=2.10 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (3.11.0)\n",
            "Requirement already satisfied: jsonschema>=2.6.0 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (4.19.2)\n",
            "Requirement already satisfied: numpy>=1.18 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (1.26.4)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (2.2.2)\n",
            "Requirement already satisfied: ruamel-yaml>=0.16 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (0.17.21)\n",
            "Requirement already satisfied: scipy>=1.4 in /root/anaconda3/lib/python3.12/site-packages (from hdmf<4,>=2.5.6->ndx-pose) (1.13.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /root/anaconda3/lib/python3.12/site-packages (from pynwb<3,>=1.5.0->ndx-pose) (2.9.0.post0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /root/anaconda3/lib/python3.12/site-packages (from jsonschema>=2.6.0->hdmf<4,>=2.5.6->ndx-pose) (23.1.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /root/anaconda3/lib/python3.12/site-packages (from jsonschema>=2.6.0->hdmf<4,>=2.5.6->ndx-pose) (2023.7.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /root/anaconda3/lib/python3.12/site-packages (from jsonschema>=2.6.0->hdmf<4,>=2.5.6->ndx-pose) (0.30.2)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /root/anaconda3/lib/python3.12/site-packages (from jsonschema>=2.6.0->hdmf<4,>=2.5.6->ndx-pose) (0.10.6)\n",
            "Requirement already satisfied: pytz>=2020.1 in /root/anaconda3/lib/python3.12/site-packages (from pandas>=1.0.5->hdmf<4,>=2.5.6->ndx-pose) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /root/anaconda3/lib/python3.12/site-packages (from pandas>=1.0.5->hdmf<4,>=2.5.6->ndx-pose) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /root/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.7.3->pynwb<3,>=1.5.0->ndx-pose) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install ndx-pose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HG8_BB3JCHni",
        "outputId": "90fcc09d-2d5e-4443-f0fb-9622fe8a5e57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1nN9XWL2L-ZeSb5rqu38gC28EOVlYuaOq\n",
            "From (redirected): https://drive.google.com/uc?id=1nN9XWL2L-ZeSb5rqu38gC28EOVlYuaOq&confirm=t&uuid=f6e015d8-9625-4b60-a583-959012baf7ac\n",
            "To: /content/save_data_AVG.mat\n",
            "100% 467M/467M [00:06<00:00, 75.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --fuzzy https://drive.google.com/file/d/1nN9XWL2L-ZeSb5rqu38gC28EOVlYuaOq/view?usp=drive_link"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3HdLigI8Cf97",
        "outputId": "0b11e244-ba33-417e-9c73-469827eb628c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['__header__', '__version__', '__globals__', 'pred', 'data', 'p_max', 'sampleID', 'metadata'])\n",
            "(360000, 3, 23)\n",
            "(360000, 23)\n"
          ]
        }
      ],
      "source": [
        "from scipy.io import loadmat\n",
        "import datetime\n",
        "import numpy as np\n",
        "from pynwb import NWBFile, NWBHDF5IO\n",
        "from ndx_pose import PoseEstimationSeries, PoseEstimation\n",
        "\n",
        "\n",
        "mat = loadmat(\"save_data_AVG.mat\")\n",
        "print(mat.keys())\n",
        "print(mat[\"data\"].shape)\n",
        "print(mat[\"p_max\"].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7MtC-C3DFAX",
        "outputId": "ccc1e9c2-1f3c-418d-f9c6-4fa1e7975e26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "augment_brightness: [[array([[0]])]]\n",
            "augment_hue: [[array([[0]])]]\n",
            "com_exp: [[array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_21_1/20201221_163226_Label3D_dannce.mat'],\n",
            "                dtype='<U115'),)                                                                                                         ]],\n",
            "        dtype=[('label3d_file', 'O')])                                                                                                      ]]\n",
            "com_file: [[array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_22_1/COM/predict00/com3d.mat'],\n",
            "        dtype='<U104')                                                                                               ]]\n",
            "com_predict_dir: [[array(['./COM/predict00'], dtype='<U15')]]\n",
            "com_predict_weights: [[array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_21_1/COM/train00/fullmodel_weights/fullmodel_end.hdf5'],\n",
            "        dtype='<U129')                                                                                                                        ]]\n",
            "com_train_dir: [[array(['./COM/train00'], dtype='<U13')]]\n",
            "dannce_predict_dir: [[array(['./DANNCE/predict03/'], dtype='<U19')]]\n",
            "dannce_predict_model: [[array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/bud/2021_06_21_1/DANNCE/train02/weights.178-2.65476.hdf5'],\n",
            "        dtype='<U120')                                                                                                               ]]\n",
            "dannce_train_dir: [[array(['./DANNCE/train02/'], dtype='<U17')]]\n",
            "exp: [[array([[array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/2/COM/predict00/com3d.mat'],\n",
            "                        dtype='<U81'), array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/2/20201107_164529_Label3D_dannce.mat'],\n",
            "                        dtype='<U92'))                                                                                                        ]],\n",
            "                dtype=[('com_file', 'O'), ('label3d_file', 'O')])                                                                                ,\n",
            "          array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/3/COM/predict00/com3d.mat'],\n",
            "                        dtype='<U81'), array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/3/total_dannce.mat'],\n",
            "                        dtype='<U74'))                                                                                      ]],\n",
            "                dtype=[('com_file', 'O'), ('label3d_file', 'O')])                                                              ,\n",
            "          array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/5/COM/predict00/com3d.mat'],\n",
            "                        dtype='<U81'), array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/test/5/total_dannce.mat'],\n",
            "                        dtype='<U74'))                                                                                      ]],\n",
            "                dtype=[('com_file', 'O'), ('label3d_file', 'O')])                                                              ,\n",
            "          array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_21_1/COM/predict00/com3d.mat'],\n",
            "                        dtype='<U104'), array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_21_1/20201223_152529_Label3D_dannce.mat'],\n",
            "                        dtype='<U115'))                                                                                                                               ]],\n",
            "                dtype=[('com_file', 'O'), ('label3d_file', 'O')])                                                                                                        ,\n",
            "          array([[(array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_26_1/COM/predict00/com3d.mat'],\n",
            "                        dtype='<U104'), array(['/n/holylfs02/LABS/olveczky_lab/Everyone/dannce_rig/dannce_ephys/art/2020_12_26_1/20210122_154646_Label3D_dannce.mat'],\n",
            "                        dtype='<U115'))                                                                                                                               ]],\n",
            "                dtype=[('com_file', 'O'), ('label3d_file', 'O')])                                                                                                        ]],\n",
            "        dtype=object)                                                                                                                                                       ]]\n",
            "left_keypoints: [[array([[ 1,  7,  8,  9, 10, 15, 16, 17, 18]])]]\n",
            "loss: [[array(['mask_nan_l1_loss'], dtype='<U16')]]\n",
            "mirror_augmentation: [[array([[1]])]]\n",
            "num_validation_per_exp: [[array([[5]])]]\n",
            "right_keypoints: [[array([[ 2, 11, 12, 13, 14, 19, 20, 21, 22]])]]\n",
            "io_config: [[array(['io.yaml'], dtype='<U7')]]\n",
            "crop_height: [[array([[   0, 1200]])]]\n",
            "crop_width: [[array([[   0, 1920]])]]\n",
            "n_channels_in: [[array([[3]])]]\n",
            "camnames: [[array(['0_Camera1', '0_Camera2', '0_Camera3', '0_Camera4', '0_Camera5',\n",
            "         '0_Camera6'], dtype='<U9')                                      ]]\n",
            "downfac: [[array([[1]])]]\n",
            "n_views: [[array([[6]])]]\n",
            "n_channels_out: [[array([[20]])]]\n",
            "new_n_channels_out: [[array([[23]])]]\n",
            "new_last_kernel_size: [[array([[3, 3, 3]])]]\n",
            "batch_size: [[array([[2]])]]\n",
            "sigma: [[array([[10]])]]\n",
            "epochs: [[array([[250]])]]\n",
            "verbose: [[array([[1]])]]\n",
            "lr: [[array(['1e-3'], dtype='<U4')]]\n",
            "net: [[array(['finetune_AVG'], dtype='<U12')]]\n",
            "dannce_finetune_weights: [[array(['/n/holylfs02/LABS/olveczky_lab/Diego/data/dannce/weights/ratAVG2/'],\n",
            "        dtype='<U65')                                                         ]]\n",
            "train_mode: [[array(['finetune'], dtype='<U8')]]\n",
            "n_layers_locked: [[array([[0]])]]\n",
            "metric: [[array(['mse                  ', 'euclidean_distance_3D'], dtype='<U21')]]\n",
            "gpu_id: [[array(['0'], dtype='<U1')]]\n",
            "vmin: [[array([[-120]])]]\n",
            "vmax: [[array([[120]])]]\n",
            "nvox: [[array([[80]])]]\n",
            "interp: [[array(['nearest'], dtype='<U7')]]\n",
            "depth: [[array([[0]])]]\n",
            "immode: [[array(['vid'], dtype='<U3')]]\n",
            "rotate: [[array([[1]])]]\n",
            "expval: [[array([[1]])]]\n",
            "com_thresh: [[array([[0]])]]\n",
            "weighted: [[array([[0]])]]\n",
            "com_method: [[array(['median'], dtype='<U6')]]\n",
            "cthresh: [[array([[10000000]])]]\n",
            "channel_combo: [[array(['None'], dtype='<U4')]]\n",
            "max_num_samples: [[array(['max'], dtype='<U3')]]\n",
            "predict_mode: [[array(['torch'], dtype='<U5')]]\n",
            "medfilt_window: [[array([[30]])]]\n",
            "rand_view_replace: [[array([[1]])]]\n",
            "n_rand_views: [[array([[6]])]]\n",
            "start_sample: [[array([[0]])]]\n",
            "comthresh: [[array([[0]])]]\n",
            "com_fromlabels: [[array([[0]])]]\n",
            "augment_continuous_rotation: [[array([[0]])]]\n",
            "cam3_train: [[array(['None'], dtype='<U4')]]\n",
            "debug_volume_tifdir: [[array(['None'], dtype='<U4')]]\n",
            "vol_size: [[array(['None'], dtype='<U4')]]\n",
            "net_type: [[array(['None'], dtype='<U4')]]\n",
            "from_weights: [[array(['None'], dtype='<U4')]]\n",
            "dannce_predict_vol_tifdir: [[array(['None'], dtype='<U4')]]\n",
            "multi_gpu_train: [[array([[0]])]]\n",
            "heatmap_reg: [[array([[0]])]]\n",
            "heatmap_reg_coeff: [[array([[0.01]])]]\n",
            "save_pred_targets: [[array([[0]])]]\n",
            "start_batch: [[array([[0]])]]\n",
            "viddir: [[array(['videos'], dtype='<U6')]]\n",
            "extension: [[array(['.mp4'], dtype='<U4')]]\n",
            "vid_dir_flag: [[array([[1]])]]\n",
            "num_train_per_exp: [[array(['None'], dtype='<U4')]]\n",
            "chunks: [[array([[None]], dtype=object)]]\n",
            "lockfirst: [[array(['None'], dtype='<U4')]]\n",
            "load_valid: [[array(['None'], dtype='<U4')]]\n",
            "mono: [[array([[0]])]]\n",
            "augment_hue_val: [[array([[0.05]])]]\n",
            "augment_bright_val: [[array([[0.05]])]]\n",
            "augment_rotation_val: [[array([[5]])]]\n",
            "drop_landmark: [[array(['None'], dtype='<U4')]]\n",
            "raw_im_h: [[array([[1200]])]]\n",
            "raw_im_w: [[array([[1920]])]]\n",
            "mirror: [[array([[0]])]]\n",
            "n_instances: [[array([[1]])]]\n",
            "write_npy: [[array(['None'], dtype='<U4')]]\n",
            "use_npy: [[array([[0]])]]\n",
            "data_split_seed: [[array(['None'], dtype='<U4')]]\n",
            "valid_exp: [[array(['None'], dtype='<U4')]]\n",
            "maxbatch: [[array([[2500]])]]\n",
            "label3d_file: [[array(['./temp_dannce.mat'], dtype='<U17')]]\n",
            "base_exp_folder: [[array(['.'], dtype='<U1')]]\n",
            "chan_num: [[array([[3]])]]\n"
          ]
        }
      ],
      "source": [
        "for n in mat[\"metadata\"].dtype.names:\n",
        "    x = mat[\"metadata\"][n]\n",
        "    print(f\"{n}: {x}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "HrRVvYcwEQuh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(360000, 23)\n",
            "node_name  part_00\n",
            "node_name  part_01\n",
            "node_name  part_02\n",
            "node_name  part_03\n",
            "node_name  part_04\n",
            "node_name  part_05\n",
            "node_name  part_06\n",
            "node_name  part_07\n",
            "node_name  part_08\n",
            "node_name  part_09\n",
            "node_name  part_10\n",
            "node_name  part_11\n",
            "node_name  part_12\n",
            "node_name  part_13\n",
            "node_name  part_14\n",
            "node_name  part_15\n",
            "node_name  part_16\n",
            "node_name  part_17\n",
            "node_name  part_18\n",
            "node_name  part_19\n",
            "node_name  part_20\n",
            "node_name  part_21\n",
            "node_name  part_22\n"
          ]
        }
      ],
      "source": [
        "nwb_filename = 'save_data_AVG_2.nwb'\n",
        "\n",
        "# Pull out core data.\n",
        "trx = mat[\"pred\"]\n",
        "confidences = mat[\"p_max\"]\n",
        "print(confidences.shape)\n",
        "n_frames, xyz, n_nodes = trx.shape\n",
        "\n",
        "# TODO: replace this with the actual node names (not in the MAT file metadata?)\n",
        "node_names = [f\"part_{i:02}\" for i in range(n_nodes)]\n",
        "\n",
        "fps = 30\n",
        "#timestamps = np.arange(n_frames) / 30  # a timestamp for every frame\n",
        "timestamps = np.arange(999) / 30  # a timestamp for every frame\n",
        "\n",
        "nwbfile = NWBFile(\n",
        "    session_description='session_description',\n",
        "    identifier='identifier',\n",
        "    session_start_time=datetime.datetime.now(datetime.timezone.utc)\n",
        ")\n",
        "\n",
        "pose_estimation_series = []\n",
        "for i, node_name in enumerate(node_names):\n",
        "    print(\"node_name \", node_name)\n",
        "    data = trx[0:999, :, i]  # num_frames x (x, y, z)\n",
        "    confidence = confidences[0:999, i]  # a confidence value for every node on every frame\n",
        "\n",
        "    node_series = PoseEstimationSeries(\n",
        "        name=node_name,\n",
        "        description='',\n",
        "        data=data,\n",
        "        unit='pixels',\n",
        "        reference_frame='',\n",
        "        timestamps=timestamps,\n",
        "        confidence=confidence,\n",
        "        confidence_definition='',\n",
        "    )\n",
        "\n",
        "    pose_estimation_series.append(node_series)\n",
        "\n",
        "\n",
        "pe = PoseEstimation(\n",
        "    pose_estimation_series=pose_estimation_series,\n",
        "    description='',\n",
        "    # original_videos=[''],\n",
        "    # labeled_videos=[''],\n",
        "    # dimensions=[],\n",
        "    # scorer='',\n",
        "    source_software='DANNCE',\n",
        "    source_software_version='',\n",
        "    nodes=node_names,\n",
        "    edges=[],  # TODO: Add edges for viz?\n",
        ")\n",
        "\n",
        "behavior_pm = nwbfile.create_processing_module(\n",
        "    name='behavior',\n",
        "    description='processed behavioral data'\n",
        ")\n",
        "behavior_pm.add(pe)\n",
        "\n",
        "with NWBHDF5IO(nwb_filename, mode='w') as io:\n",
        "    io.write(nwbfile)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pebgstClGfMf",
        "outputId": "1c9bbccc-e9d1-4653-aeba-6c635fa88407"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(999, 3, 23)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Test reading the data back in and converting to concrete numpy array.\n",
        "with NWBHDF5IO(nwb_filename, mode='r', load_namespaces=True) as io:\n",
        "    read_nwbfile = io.read()\n",
        "    #print((type(read_nwbfile)))\n",
        "    read_nwbfile.stimulus\n",
        "    read_pe = read_nwbfile.processing['behavior']['PoseEstimation']\n",
        "\n",
        "    node_names = read_pe.nodes[:].tolist()\n",
        "\n",
        "    trx = []\n",
        "    for node_name in node_names:\n",
        "        #print(\"node_name: \", node_name)\n",
        "        trx.append(read_pe[node_name].data[:])\n",
        "\n",
        "    trx = np.stack(trx, axis=-1)\n",
        "\n",
        "trx.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset: file_create_date\n",
            "  Size: 1\n",
            "  Shape: (1,)\n",
            "  Data Type: object\n",
            "Dataset: identifier\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: processing/behavior/PoseEstimation/description\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: processing/behavior/PoseEstimation/edges\n",
            "  Size: 0\n",
            "  Shape: (0,)\n",
            "  Data Type: uint8\n",
            "Dataset: processing/behavior/PoseEstimation/nodes\n",
            "  Size: 23\n",
            "  Shape: (23,)\n",
            "  Data Type: object\n",
            "Dataset: processing/behavior/PoseEstimation/part_00/confidence\n",
            "  Size: 999\n",
            "  Shape: (999,)\n",
            "  Data Type: float64\n",
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            "Dataset: processing/behavior/PoseEstimation/part_12/confidence\n",
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            "Dataset: processing/behavior/PoseEstimation/part_14/confidence\n",
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            "Dataset: processing/behavior/PoseEstimation/part_14/reference_frame\n",
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            "Dataset: processing/behavior/PoseEstimation/part_17/reference_frame\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
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            "Dataset: processing/behavior/PoseEstimation/part_18/confidence\n",
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            "Dataset: processing/behavior/PoseEstimation/part_18/timestamps\n",
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            "Dataset: processing/behavior/PoseEstimation/part_19/confidence\n",
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            "  Shape: (999,)\n",
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            "  Shape: ()\n",
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            "Dataset: processing/behavior/PoseEstimation/part_21/timestamps\n",
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            "  Shape: (999,)\n",
            "  Data Type: float64\n",
            "Dataset: processing/behavior/PoseEstimation/part_22/data\n",
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            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: session_description\n",
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            "Dataset: specifications/core/2.7.0/nwb.behavior\n",
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            "Dataset: specifications/core/2.7.0/nwb.device\n",
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            "Dataset: specifications/core/2.7.0/nwb.ophys\n",
            "  Size: 1\n",
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            "  Data Type: object\n",
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            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: specifications/hdmf-common/1.8.0/base\n",
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            "Dataset: specifications/hdmf-common/1.8.0/namespace\n",
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            "  Data Type: object\n",
            "Dataset: specifications/hdmf-common/1.8.0/table\n",
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            "  Data Type: object\n",
            "Dataset: specifications/hdmf-experimental/0.5.0/resources\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: specifications/ndx-pose/0.1.1/namespace\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: specifications/ndx-pose/0.1.1/ndx-pose.extensions\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n",
            "Dataset: timestamps_reference_time\n",
            "  Size: 1\n",
            "  Shape: ()\n",
            "  Data Type: object\n"
          ]
        }
      ],
      "source": [
        "import h5py\n",
        "\n",
        "# Path to the NWB file\n",
        "file_path = nwb_filename\n",
        "\n",
        "# Open the NWB file\n",
        "with h5py.File(file_path, 'r') as f:\n",
        "    # Traverse the file and print the size of each dataset\n",
        "    def print_dataset_info(name, obj):\n",
        "        if isinstance(obj, h5py.Dataset):\n",
        "            print(f\"Dataset: {name}\")\n",
        "            print(f\"  Size: {obj.size}\")\n",
        "            print(f\"  Shape: {obj.shape}\")\n",
        "            print(f\"  Data Type: {obj.dtype}\")\n",
        "\n",
        "    f.visititems(print_dataset_info)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.12.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}