awkward-tractogram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path as op\n",
    "import awkward as ak\n",
    "import vector\n",
    "from dipy.io.streamline import load_tractogram\n",
    "import numpy as np\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector.register_awkward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = boto3.client('s3')\n",
    "subject = 100206\n",
    "clean_fname = (f\"sub-{subject}_dwi_space-RASMM_model-CSD_desc-prob-afq-clean_tractography\")\n",
    "if not op.exists(\"example.trk\"):\n",
    "    s3.download_file(\n",
    "        \"open-neurodata\",f\"rokem/hcp1200/afq/sub-{subject}/ses-01/{clean_fname}.trk\",\n",
    "        \"example.trk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trk = load_tractogram(\"example.trk\", reference=\"same\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14743325"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(trk.streamlines._lengths)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14743325, 3), (54477,), 14742970)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trk.streamlines._data.shape, np.diff(trk.streamlines._offsets).shape, np.sum(np.diff(trk.streamlines._offsets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "trk_ak = ak.unflatten(trk.streamlines._data, trk.streamlines._lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "trk_ak = ak.zip({\"x\": trk_ak[:, :, 0], \"y\": trk_ak[:, :, 1], \"z\": trk_ak[:, :, 2]}, with_name=\"Vector3D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is there any way to do this with a collection of points (instead of just one)?\n",
    "is_close = ak.argmin(abs(trk_ak - vector.VectorObject3D(x=0, y=0, z=0)) < 10, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Array [31678, 35741, 35745, 36700, ..., 37326, 37425, 54294] type='9 * int64'>,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(is_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arokem/miniconda3/envs/awkward/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from dipy.segment.bundles import bundles_distances_mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can we speed this up with Awkward?\n",
    "distances = bundles_distances_mdf(trk.streamlines, trk.streamlines)"
   ]
  }
 ],
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