diff --git a/docs/notebooks/bold_realignment.ipynb b/docs/notebooks/bold_realignment.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nibabel as nb\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "from shutil import rmtree, copy, move\n",
+    "from importlib import reload\n",
+    "import asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
+    "from scipy.ndimage import binary_dilation\n",
+    "from skimage.morphology import ball\n",
+    "\n",
+    "import nitransforms as nt\n",
+    "from nipreps.synthstrip.wrappers.nipype import SynthStrip\n",
+    "from nipype.interfaces.afni import Volreg\n",
+    "from eddymotion.registration import ants as erants\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_PATH = Path('/data/datasets/')\n",
+    "WORKDIR = Path.home() / 'tmp' / 'eddymotiondev' / 'ismrm25'\n",
+    "WORKDIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "OUTPUT_DIR = Path(\"/data/derivatives\") / \"eddymotion-ismrm25-exp2\"\n",
+    "OUTPUT_DIR.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bold_runs = [\n",
+    "    Path(line) for line in (DATA_PATH / \"ismrm_sample.txt\").read_text().splitlines()\n",
+    "    if line.strip()\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "for bold_run in bold_runs:\n",
+    "    avg_path = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_boldref.nii.gz\"\n",
+    "\n",
+    "    if not avg_path.exists():\n",
+    "        nii = nb.load(DATA_PATH / bold_run)\n",
+    "        average = nii.get_fdata().mean(-1)\n",
+    "        avg_path.parent.mkdir(exist_ok=True, parents=True)\n",
+    "        nii.__class__(\n",
+    "            average,\n",
+    "            nii.affine,\n",
+    "            nii.header\n",
+    "        ).to_filename(avg_path)\n",
+    "\n",
+    "    bmask_path = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_label-brain_mask.nii.gz\"\n",
+    "    if not bmask_path.exists():\n",
+    "        bmsk_results = SynthStrip(\n",
+    "            in_file=str(avg_path),\n",
+    "            use_gpu=True,\n",
+    "        ).run(cwd=str(WORKDIR))\n",
+    "        copy(bmsk_results.outputs.out_mask, bmask_path)\n",
+    "\n",
+    "    dilmask_path = avg_path.parent / f\"{avg_path.name.rsplit('_', 1)[0]}_label-braindilated_mask.nii.gz\"\n",
+    "\n",
+    "    if not dilmask_path.exists():\n",
+    "        niimsk = nb.load(bmask_path)\n",
+    "        niimsk.__class__(\n",
+    "                binary_dilation(niimsk.get_fdata() > 0.0, ball(4)).astype(\"uint8\"),\n",
+    "                niimsk.affine,\n",
+    "                niimsk.header,\n",
+    "            ).to_filename(dilmask_path)\n",
+    "\n",
+    "    oned_matrix_path = avg_path.parent /  f\"{avg_path.name.rsplit('_', 1)[0]}_desc-hmc_xfm.txt\"\n",
+    "    realign_output = avg_path.parent /  f\"{avg_path.name.rsplit('_', 1)[0]}_desc-realigned_bold.nii.gz\"\n",
+    "\n",
+    "    if not realign_output.exists():\n",
+    "        volreg_results = Volreg(\n",
+    "            in_file=str(DATA_PATH / bold_run),\n",
+    "            in_weight_volume=str(dilmask_path),\n",
+    "            args='-Fourier -twopass',\n",
+    "            zpad=4,\n",
+    "            outputtype='NIFTI_GZ',\n",
+    "            oned_matrix_save=f\"{oned_matrix_path}.aff12.1D\",\n",
+    "            out_file=str(realign_output),\n",
+    "            num_threads=12,\n",
+    "        ).run(cwd=str(WORKDIR))\n",
+    "\n",
+    "        move(volreg_results.outputs.oned_matrix_save, oned_matrix_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afni_realigned = [\n",
+    "    OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-realigned_bold.nii.gz\"\n",
+    "    for bold_run in bold_runs\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000005/sub-01/func/sub-01_task-mixedgamblestask_run-03_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000005/sub-02/func/sub-02_task-mixedgamblestask_run-01_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000005/sub-06/func/sub-06_task-mixedgamblestask_run-02_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000005/sub-15/func/sub-15_task-mixedgamblestask_run-03_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000005/sub-14/func/sub-14_task-mixedgamblestask_run-01_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000030/sub-10570/func/sub-10570_task-bht_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000030/sub-10460/func/sub-10460_task-scap_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000030/sub-10304/func/sub-10304_task-bart_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000030/sub-10376/func/sub-10376_task-taskswitch_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000030/sub-11097/func/sub-11097_task-stopsignal_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000210/sub-28/func/sub-28_task-cuedSGT_run-02_echo-1_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000210/sub-16/func/sub-16_task-cuedSGT_run-02_echo-1_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000210/sub-24/func/sub-24_task-cuedSGT_run-03_echo-1_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000210/sub-18/func/sub-18_task-cuedSGT_run-04_echo-1_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds000210/sub-09/func/sub-09_task-cuedSGT_run-03_echo-1_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds002785/sub-0202/func/sub-0202_task-workingmemory_acq-seq_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds002785/sub-0214/func/sub-0214_task-anticipation_acq-seq_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds002785/sub-0139/func/sub-0139_task-restingstate_acq-mb3_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds002785/sub-0178/func/sub-0178_task-workingmemory_acq-seq_desc-realigned_bold.nii.gz'),\n",
+       " PosixPath('/data/derivatives/eddymotion-ismrm25-exp2/ds002785/sub-0016/func/sub-0016_task-emomatching_acq-seq_desc-realigned_bold.nii.gz')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afni_realigned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def plot_profile(image_path, axis=None, indexing=None, cmap='gray', label=None, figsize=(15, 1.7)):\n",
+    "    \"\"\"Plots a single image slice on a given axis or a new figure if axis is None.\"\"\"\n",
+    "    # Load the image\n",
+    "    image_data = nb.load(image_path).get_fdata()\n",
+    "    \n",
+    "    # Define default indexing if not provided\n",
+    "    if indexing is None:\n",
+    "        indexing = (image_data.shape[0] // 2, 3 * image_data.shape[1] // 4, slice(None), slice(None))\n",
+    "    \n",
+    "    # If no axis is provided, create a new figure and axis\n",
+    "    if axis is None:\n",
+    "        fig, axis = plt.subplots(figsize=figsize)\n",
+    "    else:\n",
+    "        fig = None  # If axis is provided, we won't manage the figure\n",
+    "    \n",
+    "    # Display the image on the specified axis with aspect='auto' and the colormap\n",
+    "    axis.imshow(image_data[indexing], aspect='auto', cmap=cmap)\n",
+    "    \n",
+    "    # Turn off the axis for a cleaner look\n",
+    "    axis.axis('off')\n",
+    "    \n",
+    "    if label:\n",
+    "        # Annotate the plot with the provided label\n",
+    "        axis.text(0.02, 0.95, label, color='white', fontsize=12, ha='left', va='top', transform=axis.transAxes)\n",
+    "    \n",
+    "    # If we created the figure, show it\n",
+    "    if fig is not None:\n",
+    "        plt.show()\n",
+    "    \n",
+    "    return fig\n",
+    "\n",
+    "# def plot_combined_profile(images, indexing=None, figsize=(15, 1.7), cmap='gray', labels=None):\n",
+    "#     # Create a figure with three subplots in a vertical layout and specified figure size\n",
+    "#     n_images = len(images)\n",
+    "\n",
+    "#     nplots = n_images * len(indexing or [True])\n",
+    "#     figsize = (figsize[0], figsize[1] * nplots)\n",
+    "#     fig, axes = plt.subplots(nplots, 1, figsize=figsize, constrained_layout=True)\n",
+    "    \n",
+    "#     if labels is None or isinstance(labels, str):\n",
+    "#         labels = (labels, ) * nplots\n",
+    "    \n",
+    "#     if indexing is None or len(indexing) == 0:\n",
+    "#         indexing = [None]\n",
+    "    \n",
+    "#     for i, idx in enumerate(indexing):\n",
+    "#         for j in range(len(images)):\n",
+    "#             ax = axes[i * n_images + j]\n",
+    "#             plot_profile(images[j], axis=ax, indexing=idx, cmap=cmap, label=labels[j])\n",
+    "    \n",
+    "#     return fig\n",
+    "\n",
+    "def plot_combined_profile(images, afni_fd, eddymotion_fd, indexing=None, figsize=(15, 1.7), cmap='gray', labels=None):\n",
+    "    # Calculate the number of profile plots\n",
+    "    n_images = len(images)\n",
+    "    nplots = n_images * len(indexing or [True])\n",
+    "    total_height = figsize[1] * nplots + 2  # Adjust figure height for FD plot\n",
+    "\n",
+    "    # Create a figure with one extra row for the FD plot, setting `sharex=True` for shared x-axis\n",
+    "    fig, axes = plt.subplots(nplots + 1, 1, figsize=(figsize[0], total_height), constrained_layout=True, sharex=True)\n",
+    "\n",
+    "    # Plot the framewise displacement on the first axis\n",
+    "    fd_axis = axes[0]\n",
+    "    timepoints = np.arange(len(afni_fd))  # Assuming afni_fd and eddymotion_fd have the same length\n",
+    "    fd_axis.plot(timepoints, afni_fd, label='AFNI 3dVolreg FD', color='blue')\n",
+    "    fd_axis.plot(timepoints, eddymotion_fd, label='eddymotion FD', color='orange')\n",
+    "    fd_axis.set_ylabel(\"FD (mm)\")\n",
+    "    fd_axis.legend(loc=\"upper right\")\n",
+    "    fd_axis.set_xticks([])  # Hide x-ticks to keep x-axis clean\n",
+    "\n",
+    "    # Set labels for profile plots if not provided\n",
+    "    if labels is None or isinstance(labels, str):\n",
+    "        labels = (labels,) * nplots\n",
+    "\n",
+    "    # Set indexing if not provided\n",
+    "    if indexing is None or len(indexing) == 0:\n",
+    "        indexing = [None]\n",
+    "    \n",
+    "    # Plot each profile slice below the FD plot\n",
+    "    for i, idx in enumerate(indexing):\n",
+    "        for j in range(len(images)):\n",
+    "            ax = axes[i * n_images + j + 1]  # Shift index by 1 to account for FD plot\n",
+    "            plot_profile(images[j], axis=ax, indexing=idx, cmap=cmap, label=labels[j])\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x510 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_combined_profile((DATA_PATH / bold_runs[15], afni_realigned[15], afni_realigned[15]), labels=(\"hmc1\", \"original\", \"hmc2\"));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datashape = nb.load(DATA_PATH / bold_runs[15]).shape\n",
+    "plot_profile(DATA_PATH / bold_runs[15], afni_realigned[15], afni_realigned[15],\n",
+    "             indexing=(slice(None), 3 * datashape[1] // 4, datashape[2] // 2, slice(None)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from eddymotion.estimator import EddyMotionEstimator\n",
+    "from eddymotion.model.base import AverageModel\n",
+    "from eddymotion.utils import random_iterator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async def ants(t, data, hdr, nii, brainmask_path, semaphore, workdir):\n",
+    "    async with semaphore:\n",
+    "        # Set up paths\n",
+    "        fixed_path = workdir / f\"fixedimage_{t:04d}.nii.gz\"\n",
+    "        moving_path = workdir / f\"movingimage_{t:04d}.nii.gz\"\n",
+    "\n",
+    "        # Create a mask for the specific timepoint\n",
+    "        t_mask = np.zeros(data.shape[-1], dtype=bool)\n",
+    "        t_mask[t] = True\n",
+    "\n",
+    "        # Fit and predict using the model\n",
+    "        model = AverageModel()\n",
+    "        model.fit(\n",
+    "            data[..., ~t_mask],\n",
+    "            stat=\"median\",\n",
+    "        )\n",
+    "        fixed_data = model.predict()\n",
+    "\n",
+    "        # Save fixed and moving images\n",
+    "        nii.__class__(fixed_data, nii.affine, hdr).to_filename(fixed_path)\n",
+    "        nii.__class__(data[..., t_mask], nii.affine, hdr).to_filename(moving_path)\n",
+    "\n",
+    "        # Generate the command\n",
+    "        cmdline = erants.generate_command(\n",
+    "            fixed_path,\n",
+    "            moving_path,\n",
+    "            fixedmask_path=brainmask_path,\n",
+    "            output_transform_prefix=f\"conversion-{t:02d}\",\n",
+    "            num_threads=8,\n",
+    "        )\n",
+    "\n",
+    "        # Run the command\n",
+    "        proc = await asyncio.create_subprocess_shell(\n",
+    "            cmdline,\n",
+    "            cwd=str(workdir),\n",
+    "            stdout=(workdir / f\"ants-{t:04d}.out\").open(\"w+\"),\n",
+    "            stderr=(workdir / f\"ants-{t:04d}.err\").open(\"w+\"),\n",
+    "        )\n",
+    "        returncode = await proc.wait()\n",
+    "        return returncode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ds000005/sub-01/func\n",
+      "ds000005/sub-02/func\n",
+      "ds000005/sub-06/func\n",
+      "ds000005/sub-15/func\n",
+      "ds000005/sub-14/func\n",
+      "ds000030/sub-10570/func\n",
+      "ds000030/sub-10460/func\n",
+      "ds000030/sub-10304/func\n",
+      "ds000030/sub-10376/func\n",
+      "ds000030/sub-11097/func\n",
+      "ds000210/sub-28/func\n",
+      "ds000210/sub-16/func\n",
+      "ds000210/sub-24/func\n",
+      "ds000210/sub-18/func\n",
+      "ds000210/sub-09/func\n",
+      "ds002785/sub-0202/func\n",
+      "ds002785/sub-0214/func\n",
+      "ds002785/sub-0139/func\n",
+      "ds002785/sub-0178/func\n",
+      "ds002785/sub-0016/func\n"
+     ]
+    },
+    {
+     "ename": "CancelledError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCancelledError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 26\u001b[0m\n\u001b[1;32m     23\u001b[0m         tasks\u001b[38;5;241m.\u001b[39mappend(task)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Await all tasks\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mgather(\u001b[38;5;241m*\u001b[39mtasks, return_exceptions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/tasks.py:349\u001b[0m, in \u001b[0;36mTask.__wakeup\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__wakeup\u001b[39m(\u001b[38;5;28mself\u001b[39m, future):\n\u001b[1;32m    348\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 349\u001b[0m         \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    351\u001b[0m         \u001b[38;5;66;03m# This may also be a cancellation.\u001b[39;00m\n\u001b[1;32m    352\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__step(exc)\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/tasks.py:279\u001b[0m, in \u001b[0;36mTask.__step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    277\u001b[0m         result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 279\u001b[0m         result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_must_cancel:\n\u001b[1;32m    282\u001b[0m         \u001b[38;5;66;03m# Task is cancelled right before coro stops.\u001b[39;00m\n",
+      "Cell \u001b[0;32mIn[12], line 2\u001b[0m, in \u001b[0;36mants\u001b[0;34m(t, data, hdr, nii, brainmask_path, semaphore, workdir)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mants\u001b[39m(t, data, hdr, nii, brainmask_path, semaphore, workdir):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m semaphore:\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;66;03m# Set up paths\u001b[39;00m\n\u001b[1;32m      4\u001b[0m         fixed_path \u001b[38;5;241m=\u001b[39m workdir \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfixedimage_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m04d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.nii.gz\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      5\u001b[0m         moving_path \u001b[38;5;241m=\u001b[39m workdir \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmovingimage_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m04d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.nii.gz\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/locks.py:15\u001b[0m, in \u001b[0;36m_ContextManagerMixin.__aenter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__aenter__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m---> 15\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39macquire()\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;66;03m# We have no use for the \"as ...\"  clause in the with\u001b[39;00m\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;66;03m# statement for locks.\u001b[39;00m\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/locks.py:387\u001b[0m, in \u001b[0;36mSemaphore.acquire\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 387\u001b[0m         \u001b[38;5;28;01mawait\u001b[39;00m fut\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_waiters\u001b[38;5;241m.\u001b[39mremove(fut)\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/futures.py:287\u001b[0m, in \u001b[0;36mFuture.__await__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m    286\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_asyncio_future_blocking \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 287\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m  \u001b[38;5;66;03m# This tells Task to wait for completion.\u001b[39;00m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m    289\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mawait wasn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt used with future\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/tasks.py:349\u001b[0m, in \u001b[0;36mTask.__wakeup\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__wakeup\u001b[39m(\u001b[38;5;28mself\u001b[39m, future):\n\u001b[1;32m    348\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 349\u001b[0m         \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    351\u001b[0m         \u001b[38;5;66;03m# This may also be a cancellation.\u001b[39;00m\n\u001b[1;32m    352\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__step(exc)\n",
+      "File \u001b[0;32m~/.miniconda/envs/eddymotion/lib/python3.11/asyncio/futures.py:198\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m _CANCELLED:\n\u001b[1;32m    197\u001b[0m     exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_cancelled_error()\n\u001b[0;32m--> 198\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m!=\u001b[39m _FINISHED:\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mInvalidStateError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResult is not ready.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mCancelledError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# Set up concurrency limit and tasks\n",
+    "semaphore = asyncio.Semaphore(12)\n",
+    "tasks = []\n",
+    "\n",
+    "# Load and preprocess data\n",
+    "for bold_run in bold_runs:\n",
+    "    print(bold_run.parent)\n",
+    "    workdir = WORKDIR / bold_run.parent\n",
+    "    workdir.mkdir(parents=True, exist_ok=True)\n",
+    "    data_path = DATA_PATH / bold_run\n",
+    "    brainmask_path = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_label-brain_mask.nii.gz\"\n",
+    "\n",
+    "    nii = nb.load(data_path)\n",
+    "    hdr = nii.header.copy()\n",
+    "    hdr.set_sform(nii.affine, code=1)\n",
+    "    hdr.set_qform(nii.affine, code=1)\n",
+    "    data = nii.get_fdata(dtype=\"float32\")\n",
+    "    n_timepoints = data.shape[-1]\n",
+    "\n",
+    "    # Start tasks immediately upon creation\n",
+    "    for t in random_iterator(n_timepoints):  # Random iterator\n",
+    "        task = asyncio.create_task(ants(t, data, hdr, nii, brainmask_path, semaphore, workdir))\n",
+    "        tasks.append(task)\n",
+    "\n",
+    "# Await all tasks\n",
+    "results = await asyncio.gather(*tasks, return_exceptions=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nitransforms.resampling import apply\n",
+    "from eddymotion.registration.utils import displacement_framewise\n",
+    "\n",
+    "afni_fd = {}\n",
+    "nitransforms_fd = {}\n",
+    "# Load and preprocess data\n",
+    "for bold_run in bold_runs:\n",
+    "    work_path = WORKDIR / bold_run.parent\n",
+    "\n",
+    "    n_timepoints = len(list(work_path.glob(\"*.mat\")))\n",
+    "\n",
+    "    xfms = [\n",
+    "        nt.linear.Affine(\n",
+    "            nt.io.itk.ITKLinearTransform.from_filename(\n",
+    "                work_path / f\"conversion-{t:02d}0GenericAffine.mat\"\n",
+    "            ).to_ras(\n",
+    "                reference=work_path / f\"fixedimage_{t:04d}.nii.gz\",\n",
+    "                moving=work_path / f\"movingimage_{t:04d}.nii.gz\",\n",
+    "            )\n",
+    "        )\n",
+    "        for t in range(n_timepoints)\n",
+    "    ]\n",
+    "\n",
+    "    nii = nb.load(DATA_PATH / bold_run)\n",
+    "    nitransforms_fd[str(bold_run)] = np.array([\n",
+    "        displacement_framewise(nii, xfm)\n",
+    "        for xfm in xfms\n",
+    "    ])\n",
+    "\n",
+    "    hmc_xfm = nt.linear.LinearTransformsMapping(xfms)\n",
+    "    out_nitransforms = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-nitransforms_bold.nii.gz\"\n",
+    "    if not out_nitransforms.exists():\n",
+    "        apply(\n",
+    "            hmc_xfm,\n",
+    "            spatialimage=nii,\n",
+    "            reference=nii,\n",
+    "        ).to_filename(out_nitransforms)\n",
+    "\n",
+    "    afni_xfms = nt.linear.load(\n",
+    "        OUTPUT_DIR / bold_run.parent /  f\"{bold_run.name.rsplit('_', 1)[0]}_desc-hmc_xfm.txt\"\n",
+    "    )\n",
+    "    afni_fd[str(bold_run)] = np.array([\n",
+    "        displacement_framewise(nii, afni_xfms[i])\n",
+    "        for i in range(len(afni_xfms))\n",
+    "    ])\n",
+    "\n",
+    "    out_afni = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-afni_bold.nii.gz\"\n",
+    "    if not out_afni.exists():\n",
+    "        apply(\n",
+    "            afni_xfms,\n",
+    "            spatialimage=nii,\n",
+    "            reference=nii,\n",
+    "        ).to_filename(out_afni)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ds000005/sub-01/func/sub-01_task-mixedgamblestask_run-03_bold.nii.gz': array([0.        , 0.10793175, 0.07858656, 0.18502728, 0.12220535,\n",
+       "        0.168716  , 0.14503799, 0.14865804, 0.1803173 , 0.13340028,\n",
+       "        0.21312989, 0.14758936, 0.21596067, 0.17506483, 0.20782903,\n",
+       "        0.15595841, 0.20194838, 0.15876696, 0.20505382, 0.17766943,\n",
+       "        0.22369231, 0.18314188, 0.21592902, 0.17853583, 0.229064  ,\n",
+       "        0.19048681, 0.24726324, 0.21111294, 0.24701978, 0.20154606,\n",
+       "        0.24295423, 0.20993123, 0.25803219, 0.25123738, 0.27384459,\n",
+       "        0.25426043, 0.25935159, 0.27905261, 0.27254138, 0.3148201 ,\n",
+       "        0.2813835 , 0.27881108, 0.25465829, 0.29885062, 0.27027669,\n",
+       "        0.29357876, 0.26970242, 0.2988554 , 0.24956041, 0.30005082,\n",
+       "        0.24795282, 0.29294854, 0.23768819, 0.30652736, 0.24880974,\n",
+       "        0.29505102, 0.23742751, 0.30218212, 0.24163347, 0.29045177,\n",
+       "        0.24481863, 0.26511888, 0.24801033, 0.25128672, 0.24696843,\n",
+       "        0.25947232, 0.254379  , 0.26095674, 0.26111144, 0.27838088,\n",
+       "        0.29010474, 0.27330852, 0.28997804, 0.2673482 , 0.25280404,\n",
+       "        0.26200645, 0.25685835, 0.32458219, 0.23524821, 0.2909785 ,\n",
+       "        0.25199285, 0.30207946, 0.25769067, 0.29890065, 0.26379555,\n",
+       "        0.28436454, 0.26136001, 0.27945646, 0.2660181 , 0.26950163,\n",
+       "        0.23672501, 0.26989616, 0.2274122 , 0.27240584, 0.24145814,\n",
+       "        0.30255474, 0.25040937, 0.30172481, 0.26969254, 0.28364694,\n",
+       "        0.26733306, 0.27470138, 0.23203171, 0.27756435, 0.25078429,\n",
+       "        0.2730304 , 0.26076443, 0.2542343 , 0.28148259, 0.26732483,\n",
+       "        0.295022  , 0.23473558, 0.30902055, 0.25759529, 0.31444771,\n",
+       "        0.25567231, 0.30649651, 0.25907932, 0.30073467, 0.23362245,\n",
+       "        0.2935698 , 0.24625316, 0.31459359, 0.26727161, 0.31027673,\n",
+       "        0.24809672, 0.29786052, 0.2610883 , 0.28920238, 0.26762517,\n",
+       "        0.30663702, 0.28841435, 0.30020943, 0.24208688, 0.27206367,\n",
+       "        0.23463178, 0.27058097, 0.25871453, 0.25448721, 0.32689552,\n",
+       "        0.28988766, 0.31949122, 0.25499453, 0.28692947, 0.24418622,\n",
+       "        0.30234674, 0.32318   , 0.29434893, 0.30449252, 0.26105419,\n",
+       "        0.32486832, 0.29181605, 0.35474997, 0.31843427, 0.33653931,\n",
+       "        0.30579458, 0.28427039, 0.29561227, 0.31600498, 0.35408913,\n",
+       "        0.34424495, 0.27510732, 0.36413516, 0.31584534, 0.3021935 ,\n",
+       "        0.28558072, 0.31046495, 0.35189537, 0.30334478, 0.37838875,\n",
+       "        0.32643975, 0.32898839, 0.35809492, 0.30001243, 0.297251  ,\n",
+       "        0.37888815, 0.34003041, 0.33098704, 0.36931671, 0.32952686,\n",
+       "        0.20464863, 0.33545558, 0.32234028, 0.33678325, 0.34672789,\n",
+       "        0.34819289, 0.37310942, 0.33735273, 0.36967769, 0.33477125,\n",
+       "        0.36075379, 0.33499653, 0.38501796, 0.3619614 , 0.40468146,\n",
+       "        0.35920649, 0.44377621, 0.41512527, 0.45341489, 0.41960495,\n",
+       "        0.40979256, 0.35579568, 0.4183747 , 0.35804851, 0.38398601,\n",
+       "        0.34910078, 0.42067435, 0.38778546, 0.43910712, 0.42170021,\n",
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+       "        6.58366444e-01, 6.37341555e-01, 6.50822205e-01, 6.34822388e-01,\n",
+       "        6.38264411e-01, 6.32604821e-01, 6.75950368e-01, 6.49150504e-01,\n",
+       "        6.54192349e-01, 6.60671472e-01, 6.42434085e-01, 6.39311688e-01,\n",
+       "        6.23830044e-01, 6.55864032e-01, 6.68955383e-01, 6.67932134e-01,\n",
+       "        6.85885397e-01, 7.03086424e-01, 7.19331782e-01, 7.36969415e-01,\n",
+       "        7.20140978e-01, 7.16254910e-01, 7.05309679e-01, 7.12572236e-01,\n",
+       "        7.17169216e-01, 6.95160644e-01, 7.05349319e-01, 7.13906234e-01,\n",
+       "        7.28696550e-01, 7.31559795e-01, 7.08638906e-01, 7.56734954e-01,\n",
+       "        7.66701935e-01, 7.35025355e-01, 7.82621522e-01, 7.81627679e-01,\n",
+       "        7.52638453e-01, 7.79359118e-01, 7.76153317e-01, 7.96074168e-01,\n",
+       "        7.94643051e-01, 8.12568631e-01, 8.08584499e-01, 8.09336009e-01,\n",
+       "        8.31267660e-01, 8.10774013e-01]),\n",
+       " 'ds002785/sub-0016/func/sub-0016_task-emomatching_acq-seq_bold.nii.gz': array([1.60326038e-06, 1.44742966e-01, 1.35537675e-01, 1.77457350e-01,\n",
+       "        1.57588085e-01, 1.43056844e-01, 1.96000534e-01, 2.17515409e-01,\n",
+       "        2.32461658e-01, 3.12853914e-01, 2.81444844e-01, 2.92605001e-01,\n",
+       "        2.94602074e-01, 2.85238385e-01, 2.70397518e-01, 2.75532994e-01,\n",
+       "        2.35077403e-01, 2.99282645e-01, 2.99205247e-01, 2.75346698e-01,\n",
+       "        3.86334552e-01, 3.63954252e-01, 3.14228113e-01, 3.51569250e-01,\n",
+       "        2.87686267e-01, 3.06615015e-01, 3.29233757e-01, 2.83780613e-01,\n",
+       "        3.40364449e-01, 2.80791370e-01, 2.82181900e-01, 3.06365626e-01,\n",
+       "        3.15197581e-01, 3.48133451e-01, 3.39603979e-01, 4.30536868e-01,\n",
+       "        3.71046763e-01, 4.19733161e-01, 3.84848288e-01, 4.44296779e-01,\n",
+       "        3.96853552e-01, 4.62159948e-01, 3.88061628e-01, 4.42832954e-01,\n",
+       "        4.04726741e-01, 4.48435344e-01, 4.08125138e-01, 4.69643073e-01,\n",
+       "        4.11097556e-01, 4.86051830e-01, 4.09558439e-01, 4.11893565e-01,\n",
+       "        4.57172561e-01, 3.95385661e-01, 4.80505834e-01, 4.18929074e-01,\n",
+       "        4.59661857e-01, 4.43021718e-01, 4.26981459e-01, 4.80029924e-01,\n",
+       "        4.20503939e-01, 5.04893551e-01, 4.21770605e-01, 4.75523826e-01,\n",
+       "        4.84689378e-01, 4.32183478e-01, 5.08551060e-01, 4.32407870e-01,\n",
+       "        5.15385716e-01, 4.50838156e-01, 5.17625066e-01, 4.32777680e-01,\n",
+       "        5.12091327e-01, 4.43253055e-01, 5.08823465e-01, 4.38493897e-01,\n",
+       "        4.91404429e-01, 4.73920340e-01, 4.42677398e-01, 5.30053787e-01,\n",
+       "        7.17115067e-01, 5.58920242e-01, 5.42577951e-01, 4.97348376e-01,\n",
+       "        5.17097428e-01, 5.71207450e-01, 5.29073103e-01, 5.90312462e-01,\n",
+       "        5.21288002e-01, 5.67683579e-01, 5.14119892e-01, 5.90067162e-01,\n",
+       "        5.01684348e-01, 5.85171518e-01, 5.52153513e-01, 4.96150927e-01,\n",
+       "        5.43609694e-01, 4.98656648e-01, 5.34020377e-01, 4.66129372e-01,\n",
+       "        5.65611738e-01, 4.64458234e-01, 5.31117962e-01, 5.02102083e-01,\n",
+       "        4.81750734e-01, 5.60561650e-01, 4.85589160e-01, 5.13573954e-01,\n",
+       "        5.25508819e-01, 4.89239407e-01, 5.69274373e-01, 5.12176099e-01,\n",
+       "        5.74132545e-01, 5.38199778e-01, 5.09609255e-01, 5.90564760e-01,\n",
+       "        5.24346547e-01, 5.86414494e-01, 5.82555918e-01, 5.60187834e-01,\n",
+       "        6.78856498e-01, 5.82858913e-01, 6.30397877e-01, 6.84124461e-01,\n",
+       "        5.99833438e-01, 5.45529104e-01, 6.06630773e-01, 7.33586935e-01,\n",
+       "        7.86881670e-01, 8.08341116e-01, 7.54410503e-01, 8.55731236e-01,\n",
+       "        7.96742922e-01, 8.13179271e-01, 8.05298292e-01])}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afni_fd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate an index.html with links to each SVG file\n",
+    "index_path = OUTPUT_DIR / \"index.html\"\n",
+    "with open(index_path, \"w\") as index_file:\n",
+    "    index_file.write(\"<html><body>\\n\")\n",
+    "    index_file.write(\"<h1>Profile Plot Index</h1>\\n<ul>\\n\")\n",
+    "\n",
+    "    for bold_run in bold_runs:\n",
+    "        original = DATA_PATH / bold_run\n",
+    "        nitransforms = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-nitransforms_bold.nii.gz\"\n",
+    "        afni = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-realigned_bold.nii.gz\"\n",
+    "\n",
+    "        datashape = nb.load(original).shape\n",
+    "\n",
+    "        fig = plot_combined_profile(\n",
+    "            (afni, original, nitransforms),\n",
+    "            afni_fd[str(bold_run)],\n",
+    "            nitransforms_fd[str(bold_run)],\n",
+    "            labels=(\"3dVolreg\", str(bold_run), \"eddymotion\"),\n",
+    "            indexing=(None, (slice(None), 3 * datashape[1] // 4, datashape[2] // 2, slice(None)))\n",
+    "        )\n",
+    "\n",
+    "        # Save the figure\n",
+    "        out_svg = OUTPUT_DIR / bold_run.parent / bold_run.name.replace(\".nii.gz\", \".svg\")\n",
+    "        fig.savefig(out_svg, format=\"svg\")\n",
+    "        fig.savefig(out_svg.with_suffix(\".png\"), format=\"png\", dpi=320)\n",
+    "        plt.close(fig)\n",
+    "\n",
+    "        index_file.write(f\"<li><a href={out_svg.relative_to(OUTPUT_DIR)}>{bold_run}</a></li>\\n\")\n",
+    "    \n",
+    "    index_file.write(\"</ul>\\n</body></html>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eddymotion",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}