{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51c6734c",
   "metadata": {},
   "source": [
    "# Reorientation and fragmentation\n",
    "\n",
    "One coherent story: under load, grains **reorient**; that reorientation **fragments** them into\n",
    "orientation sub-grains; and the grains that fragment the most are the **rare events**. The same\n",
    "two classes drive all of it -- `OrientationTracker` builds the IPF trajectories and\n",
    "`FragmentationAnalyzer` does the segmentation, split detection, and per-grain fragment counts.\n",
    "\n",
    "**Needs:** NEML2 v3 (orientation math) and networkit (segmentation); no external binaries. The\n",
    "simulation parts run on the shipped real CPFE run `mwe_data/cpfe_ff_fragmentation`; the\n",
    "experiment parts on the synthetic split series `mwe_data/synthetic_split_{ff,nf,ebsd}`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c5e821",
   "metadata": {},
   "source": [
    "## 1. Simulation: grains reorient under load\n",
    "\n",
    "`OrientationTracker().simulation_grain_tracks` reads each grain's `ori_rodrigues` (neml2 MRP)\n",
    "block time series; `plot_ipf_orientation_tracking` draws one IPF triangle with a start dot and a\n",
    "reorientation arrow per grain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05626330",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T23:27:54.781046Z",
     "iopub.status.busy": "2026-10-09T23:27:54.780547Z",
     "iopub.status.idle": "2026-10-09T23:27:57.466888Z",
     "shell.execute_reply": "2026-10-09T23:27:57.464402Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspace/personal/repos/graintrace/.venv/lib/python3.12/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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('out/ipf_orientation_tracking.png')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from graintrace.simulation_postprocessing import SimulationResults, FieldFileNaming\n",
    "from graintrace.ipf_orientation_tracking import OrientationTracker\n",
    "from graintrace.fragmentation import FragmentationAnalyzer\n",
    "from graintrace import plot_postprocessing as pp\n",
    "\n",
    "naming = FieldFileNaming(prefix=\"out_element_centroid\", index_width=4, sep=\"_\", suffix=\".csv\")\n",
    "res = SimulationResults(\n",
    "    \"../../mwe_data/cpfe_ff_fragmentation/out.csv\",\n",
    "    \"../../mwe_data/cpfe_ff_fragmentation/mesh_out\", field_naming=naming)\n",
    "tracker, analyzer = OrientationTracker(), FragmentationAnalyzer()\n",
    "steps = sorted(res.field_files.keys())\n",
    "tracks = tracker.simulation_grain_tracks(res, grain_ids=list(range(1, 11)), steps=steps)\n",
    "pp.plot_ipf_orientation_tracking(tracks, angle_convention=\"mrp\", output_folder=\"out\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fbfbde5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T23:27:57.471661Z",
     "iopub.status.busy": "2026-10-09T23:27:57.470907Z",
     "iopub.status.idle": "2026-10-09T23:27:57.483643Z",
     "shell.execute_reply": "2026-10-09T23:27:57.481813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "# plot_ipf_orientation_tracking writes to output_folder and does not call plt.show(),\n",
    "# so load the PNG it just wrote.\n",
    "Image(\"out/ipf_orientation_tracking.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d5ce90",
   "metadata": {},
   "source": [
    "Each dot is a grain's starting orientation, coloured by the standard IPF key; the\n",
    "black line is its path through the load steps and the arrowhead is where it ended.\n",
    "Short arrows are grains rotating barely at all, long ones are grains whose lattice is\n",
    "being dragged around by slip. This is the per-grain *average* — section 2 opens each\n",
    "grain up to see whether its elements moved together."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bfec279",
   "metadata": {},
   "source": [
    "## 2. Simulation: reorientation to fragmentation (the 1x2 figure)\n",
    "\n",
    "`FragmentationAnalyzer().grain_fragments` re-segments each grain's elements by orientation into\n",
    "sub-grains. For intragranular data pass a fixed low Leiden `gamma` (here `gamma=0.25`; the\n",
    "gamma-by-percolation sweep is only for large many-grain NF/EBSD, and `gamma=0.1` under-splits --\n",
    "it leaves most grains whole). Leiden alone will still chop a *smooth* intragranular gradient into\n",
    "several near-identical communities, so `min_subgrain_miso_deg` (here 1°) imposes a **minimum\n",
    "sub-grain misorientation**: after Leiden, fragments whose (symmetry-aware) mean orientations are\n",
    "closer than that are merged, so only genuine sub-grains -- separated by a real low-angle\n",
    "sub-boundary -- survive as distinct fragments. The signature figure puts the two views side by\n",
    "side on the **real CPFE data** and links them by two channels:\n",
    "\n",
    "- **LEFT** -- every element of each grain (`OrientationTracker().simulation_element_tracks`), so\n",
    "  the width of each fan is that grain's intragranular reorientation *spread*. Lines are thin and\n",
    "  headless (`line_width=0.8`, `show_arrow=False`) because the panel is crowded.\n",
    "- **RIGHT** -- the resulting sub-grain forks (`plot_ipf_fragmentation_tracking`): the parent\n",
    "  trajectory (neutral gray) forks at its split point into one child per fragment, small arrowhead.\n",
    "\n",
    "The linking channels are **color** and **line style**, both keyed to a per-grain *fragment* index.\n",
    "Color comes from a dark, high-contrast qualitative palette (ColorBrewer Dark2) so a grain's\n",
    "sub-grains **contrast sharply** -- pass a per-element `arrow_color` list on the left and the\n",
    "per-child `child_colors` (with `parent_color` for the gray parent) on the right. The palette is\n",
    "reused across grains (grains are told apart by their IPF *position*, sub-grains by color), and the\n",
    "start dots are drawn neutral **black** (`start_color=\"black\"`). Line style (`-`, `--`, `-.`, `:`)\n",
    "reinforces the same fragment index in the crowded left panel. So a fork on the right and the\n",
    "element cloud that formed it on the left share both color and style. Both plotters take a shared\n",
    "`ax` (returning it instead of saving) plus `line_width`, `arrow_scale`, `show_arrow`, and\n",
    "`label_fontsize`. (`arrow_color=\"ipf_final\"` is also available, tinting each track by the IPF\n",
    "color of its endpoint, when you prefer color to encode the final orientation.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2986ecb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T23:27:57.488198Z",
     "iopub.status.busy": "2026-10-09T23:27:57.487664Z",
     "iopub.status.idle": "2026-10-09T23:28:14.883089Z",
     "shell.execute_reply": "2026-10-09T23:28:14.881186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/161 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 161/161 [00:00<00:00, 94155.46it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 1089, Number of nodes: 161, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/1089 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 1089/1089 [00:00<00:00, 4145.93edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 1089/1089 [00:00<00:00, 4081.71edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 1064\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated number of edges: 1021\n",
      "\n",
      "Saving checkpoint: /tmp/segori_k0y1akkj/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n"
     ]
    },
    {
     "data": {
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       "\t\t\t{\n",
       "\t\t\t\tvar element = document.getElementById('NetworKit_script');\n",
       "\t\t\t\tif (element) {\n",
       "\t\t\t\t\telement.parentNode.removeChild(element);\n",
       "\t\t\t\t}\n",
       "\t\t\t\telement = document.createElement('script');\n",
       "\t\t\t\telement.type = 'text/javascript';\n",
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       "\t\t\n",
       "\t\t\t\t\n",
       "\t\t\t{\n",
       "\t\t\t\tvar element = document.getElementById('NetworKit_style');\n",
       "\t\t\t\tif (element) {\n",
       "\t\t\t\t\telement.parentNode.removeChild(element);\n",
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       "\t\t\t\telement = document.createElement('style');\n",
       "\t\t\t\telement.type = 'text/css';\n",
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       "\t\t\t\telement.setAttribute('id', 'NetworKit_style');\n",
       "\t\t\t\tdocument.head.appendChild(element);\n",
       "\t\t\t}\n",
       "\t\t\n",
       "\t\t\t\t\n",
       "\t\t\t{\n",
       "\t\t\t\tvar element = document.getElementById('NetworKit_Overlay');\n",
       "\t\t\t\tif (element) {\n",
       "\t\t\t\t\telement.parentNode.removeChild(element);\n",
       "\t\t\t\t}\n",
       "\t\t\t\telement = document.createElement('div');\n",
       "\t\t\t\telement.innerHTML = '<div id=\"NetworKit_Overlay_Toolbar_Top\"><div class=\"button icon-close\" id=\"NetworKit_Overlay_Close\" /></div><div id=\"NetworKit_Overlay_Title\" /> <div id=\"NetworKit_Overlay_ImageContainer\"> <div id=\"NetworKit_Overlay_Image\" /> </div> <div id=\"NetworKit_Overlay_Toolbar_Bottom\"> <div class=\"button icon-previous\" onclick=\"NetworKit_overlayImageShift(-1)\" /> <div class=\"button icon-next\" onclick=\"NetworKit_overlayImageShift(1)\" /> <a id=\"NetworKit_Overlay_Toolbar_Bottom_Save\"><div class=\"button icon-save\" /></a> </div>';\n",
       "\t\t\t\telement.setAttribute('id', 'NetworKit_Overlay');\n",
       "\t\t\t\tdocument.body.appendChild(element);\n",
       "\t\t\t\tdocument.getElementById('NetworKit_Overlay_Close').onclick = function (e) {\n",
       "\t\t\t\t\tdocument.getElementById('NetworKit_Overlay').style.display = 'none';\n",
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       "\t\t\t}\n",
       "\t\t\n",
       "\t\t\t-->\n",
       "\t\t\t</script>\n",
       "\t\t"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/61 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 61/61 [00:00<00:00, 57702.42it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 395, Number of nodes: 61, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/395 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 395/395 [00:00<00:00, 7598.90edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 395\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 379\n",
      "\n",
      "Saving checkpoint: /tmp/segori_to8jrdva/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/96 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 96/96 [00:00<00:00, 72380.58it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 638, Number of nodes: 96, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/638 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 638/638 [00:00<00:00, 7992.73edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 527\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 524\n",
      "\n",
      "Saving checkpoint: /tmp/segori_i4oa8e7q/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/98 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 98/98 [00:00<00:00, 74626.32it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 659, Number of nodes: 98, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/659 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 659/659 [00:00<00:00, 8019.47edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 615\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 590\n",
      "\n",
      "Saving checkpoint: /tmp/segori_ly_x7587/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/119 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 119/119 [00:00<00:00, 92004.09it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 803, Number of nodes: 119, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/803 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 803/803 [00:00<00:00, 7960.98edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 803/803 [00:00<00:00, 7890.57edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 765\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 735\n",
      "\n",
      "Saving checkpoint: /tmp/segori_vzpk9qqc/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/117 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 117/117 [00:00<00:00, 81843.49it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 783, Number of nodes: 117, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/783 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 783/783 [00:00<00:00, 8188.65edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 622\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 616\n",
      "\n",
      "Saving checkpoint: /tmp/segori_rw893ftn/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/149 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 149/149 [00:00<00:00, 91127.34it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 1014, Number of nodes: 149, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/1014 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 1014/1014 [00:00<00:00, 7801.42edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 1014/1014 [00:00<00:00, 7727.07edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 965\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 917\n",
      "\n",
      "Saving checkpoint: /tmp/segori_klr9jeuz/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/89 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 89/89 [00:00<00:00, 71198.37it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 586, Number of nodes: 89, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/586 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 586/586 [00:00<00:00, 8204.11edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 514\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 495\n",
      "\n",
      "Saving checkpoint: /tmp/segori_so_m97fc/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/140 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 140/140 [00:00<00:00, 91592.97it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 947, Number of nodes: 140, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/947 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 947/947 [00:00<00:00, 7769.61edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 947/947 [00:00<00:00, 7688.08edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 822\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 804\n",
      "\n",
      "Saving checkpoint: /tmp/segori_z_yjlxh2/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n",
      "\n",
      "=== Running GraphSpatialCluster ===\n",
      "\n",
      "Data loaded.\n",
      "\n",
      "Building graph with mode: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes:   0%|          | 0/63 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Building edges from nodes: 100%|██████████| 63/63 [00:00<00:00, 103746.04it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is built. Number of edges: 413, Number of nodes: 63, Number of features: 3\n",
      "\n",
      "Computing edge distances with metric: misorientation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances:   0%|          | 0/413 [00:00<?, ?edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Edge distances: 100%|██████████| 413/413 [00:00<00:00, 8811.97edge/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Keeping edges with distance <= 0.08726646259971647\n",
      "Updated number of edges: 370\n",
      "\n",
      "Converting edge distances to weights\n",
      "\n",
      "Removing edges to keep only top 12 weights per node\n",
      "Updated number of edges: 358\n",
      "\n",
      "Saving checkpoint: /tmp/segori_vweckr73/seg_ckpt (edges/weights/meta)\n",
      "\n",
      "Segmenting graph with method: leiden\n",
      "Segmenter parameters: {'gamma': 0.25}\n",
      "  NetworKit Leiden threads set to 1 (machine max 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "last = steps[-1]\n",
    "ori_cols = [\"ori_rodrigues_x\", \"ori_rodrigues_y\", \"ori_rodrigues_z\"]\n",
    "grains = list(range(1, 11))\n",
    "STYLES = [\"-\", \"--\", \"-.\", \":\"]\n",
    "# dark, high-contrast qualitative palette (ColorBrewer Dark2) keyed by fragment\n",
    "COLORS = [\"#1b9e77\", \"#d95f02\", \"#7570b3\", \"#e7298a\", \"#66a61e\", \"#a6761d\"]\n",
    "\n",
    "# min_subgrain_miso_deg=1.0: only keep a split if the sub-grains differ by >1 deg, so a\n",
    "# smooth intragranular gradient is not chopped into near-identical fragments (the mean used\n",
    "# for the merge and the fork endpoints is a symmetry-aware quaternion mean).\n",
    "frags_df, per_elem = analyzer.grain_fragments(\n",
    "    res, last, tol_deg=5.0, min_subgrain_miso_deg=1.0, seg_kwargs={\"gamma\": 0.25})\n",
    "\n",
    "# per-grain fragment-label -> (style, color): sub-grains of one grain contrast sharply\n",
    "frag_style, frag_color = {}, {}\n",
    "for g in grains:\n",
    "    labs = sorted(l for l in per_elem.values() if l.startswith(f\"{g}.\"))\n",
    "    frag_style[g] = {lab: STYLES[i % len(STYLES)] for i, lab in enumerate(labs)}\n",
    "    frag_color[g] = {lab: COLORS[i % len(COLORS)] for i, lab in enumerate(labs)}\n",
    "\n",
    "# LEFT: every element of each grain, colored + styled by the fragment it belongs to\n",
    "df0 = res.load_field_data(steps[0])\n",
    "blk0 = np.rint(df0[\"block_id\"].to_numpy()).astype(int)\n",
    "left_tracks, left_styles, left_colors = [], [], []\n",
    "for g in grains:\n",
    "    eids = df0.loc[blk0 == g, \"id\"].astype(int).tolist()\n",
    "    for eid, tr in zip(eids, tracker.simulation_element_tracks(res, element_ids=eids, steps=steps)):\n",
    "        lab = per_elem.get(eid)\n",
    "        left_tracks.append(tr)\n",
    "        left_styles.append(frag_style[g].get(lab, \"-\"))\n",
    "        left_colors.append(frag_color[g].get(lab, \"0.35\"))\n",
    "\n",
    "# RIGHT: parent (pre-last) forks into per-fragment children at the last step\n",
    "# (mean_orientation_mrp is symmetry-aware -- pass the analyzer's symmetry)\n",
    "last_df = res.load_field_data(last).set_index(\"id\")\n",
    "branches, child_styles, child_colors = [], [], []\n",
    "for g in grains:\n",
    "    parent = np.asarray([\n",
    "        analyzer.mean_orientation_mrp(\n",
    "            df[np.rint(df[\"block_id\"].to_numpy()).astype(int) == g][ori_cols].to_numpy(),\n",
    "            analyzer.symmetry)\n",
    "        for df in (res.load_field_data(int(s)) for s in steps[:-1])])\n",
    "    labs = sorted(frag_style[g])\n",
    "    children = [analyzer.mean_orientation_mrp(\n",
    "        last_df.loc[[e for e, l in per_elem.items() if l == lab], ori_cols].to_numpy(),\n",
    "        analyzer.symmetry)[None, :] for lab in labs]\n",
    "    branches.append((parent, children))\n",
    "    child_styles.append([frag_style[g][lab] for lab in labs])\n",
    "    child_colors.append([frag_color[g][lab] for lab in labs])\n",
    "\n",
    "# color = dark categorical fragment color (a grain's sub-grains contrast), black start\n",
    "# dots, gray parent, no legend; line style reinforces the fragment.\n",
    "fig, (axL, axR) = plt.subplots(1, 2, figsize=(14, 7))\n",
    "pp.plot_ipf_orientation_tracking(left_tracks, angle_convention=\"mrp\", arrow_color=left_colors,\n",
    "    start_color=\"black\", line_style=left_styles, line_width=0.8, show_arrow=False,\n",
    "    label_fontsize=18, ax=axL)\n",
    "pp.plot_ipf_fragmentation_tracking(branches, angle_convention=\"mrp\", child_colors=child_colors,\n",
    "    parent_color=\"0.35\", start_color=\"black\", child_line_styles=child_styles, line_width=2.0,\n",
    "    arrow_scale=12, show_arrow=True, label_fontsize=18, ax=axR)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"out/reorientation_vs_fragmentation.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac6cc502",
   "metadata": {},
   "source": [
    "**Reading the figure -- visualizing fragmentation.** The left panel is the *evidence* and the\n",
    "right panel is the *interpretation*. A grain whose elements stay in a tight bundle on the left is\n",
    "rotating almost rigidly (one fragment, one solid fork on the right); a grain whose elements fan\n",
    "out into two or more lobes is developing intragranular orientation gradients, and the segmenter\n",
    "turns each lobe into a distinctly colored + styled fork. Reading the two together tells you what\n",
    "kind of split it is: if the right panel forks but the left element clouds *overlap* in the IPF,\n",
    "the sub-grains are close in orientation and the split is more a **spatial gradient** than a sharp\n",
    "orientation bimodal (the graph segmenter uses spatial adjacency as well as orientation, so a\n",
    "grain's sub-grains can share orientation yet occupy different regions). The number of forks is\n",
    "controlled by the segmenter (`gamma` for Leiden resolution, `tol_deg` for the misorientation edge\n",
    "cutoff); the left fan is the ground truth you calibrate those against. The per-grain fragment\n",
    "table (`grain_id, n_fragments, fragment_sizes, n_elements`) quantifies the right panel, and the\n",
    "fragments can be viewed in 3D in ParaView: `IPFProcessor.add_element_field_to_exodus` writes a\n",
    "per-element `fragment_label` scalar, and `add_element_rgb_to_exodus` writes a per-element RGB\n",
    "(`rgb_x/y/z`, mapped to color with scalar mapping off) in the *same* dark palette as this figure.\n",
    "For the colors to be meaningful, annotate the Exodus of the **analyzed** mesh (a co-registered\n",
    "loaded run's `mesh.e`/`sim_output.e` beside the `mesh_out` you segmented) -- the shipped bare\n",
    "`cpfe_hex_fragmentation/mesh.e` is a different microstructure, so the example colors it by block\n",
    "id only as a writer demo.\n",
    "\n",
    "**Why fragmentation matters.** Intragranular fragmentation is not just descriptive: orientation\n",
    "gradients and nascent sub-boundaries are where geometrically necessary dislocations pile up, they\n",
    "are the precursors to recrystallization nuclei, and they frequently co-locate with damage\n",
    "initiation. Crucially, fragmentation is a *distinct* signal from stress or strain magnitude -- a\n",
    "grain can be only moderately stressed yet fragment heavily (favourably oriented for multi-slip),\n",
    "or highly stressed yet rotate rigidly. That independence is exactly what makes \"which grains\n",
    "fragment the most\" worth flagging as a rare event in its own right."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e896dc6e",
   "metadata": {},
   "source": [
    "**From fragmentation to rare events (REI, and REI-vs-REI plots).** Because fragmentation is an\n",
    "independent signal, it plugs into the same rare-event machinery used for stress or the Nye tensor,\n",
    "at two levels:\n",
    "\n",
    "- **Grain level** -- rank grains by `n_fragments` (or fragment-size dispersion) and select the\n",
    "  rare ones with `rare_criteria_selection_library.select_highest_scalar`, exactly the selector the\n",
    "  REI pipeline uses for scalar fields. This is the capstone in section 4 below.\n",
    "- **Field level** -- `FragmentationAnalyzer().write_reorientation_field` emits a per-element\n",
    "  `reorientation_deg` scalar (misorientation from the reference step). Feed that CSV to\n",
    "  `IdentifyRareClusters` with the `abs_scalar_diff` metric to find spatially coherent\n",
    "  rare-reorientation clusters standalone, or combine `reorientation_deg` with `nye_tensor_*` /\n",
    "  stress in a multi-feature `SimilarityMetric` so the clustering weighs fragmentation *and*\n",
    "  mechanics together.\n",
    "\n",
    "Both routes emit rare-cluster point clouds (VTK), so a **fragmentation-driven REI can be overlaid\n",
    "against a stress- or GND-driven REI** with `REIComparison` -- the REI-vs-REI overlap plot\n",
    "(IoU/Dice, per-cluster matching) that answers \"do the grains that fragment the most coincide with\n",
    "the grains that are most stressed?\". That comparison is the payoff of treating reorientation as a\n",
    "first-class REI criterion rather than a purely visual diagnostic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ee7f03",
   "metadata": {},
   "source": [
    "## 3. Experiment: detect grain splits across load steps (FF / NF / EBSD)\n",
    "\n",
    "`FragmentationAnalyzer().detect_splits` finds one grain becoming several between two steps\n",
    "(cross-step graph + connected components). FF grain tables feed it directly; NF/EBSD reach the\n",
    "same detector via `.segment` (graph/Leiden) + `.seg_to_grain_table`. The shipped synthetic\n",
    "series has mixed multiplicity (1→1, 1→2, 1→3) with a `ground_truth.json`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c836f9b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T23:28:14.888970Z",
     "iopub.status.busy": "2026-10-09T23:28:14.888745Z",
     "iopub.status.idle": "2026-10-09T23:28:14.913943Z",
     "shell.execute_reply": "2026-10-09T23:28:14.912223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    parent_a children_b  n_children child_radii\n",
      "0         2        2;6           2       10;10\n",
      "1         3      3;7;8           3    10;10;10\n",
      "2         5        5;9           2       10;10\n"
     ]
    }
   ],
   "source": [
    "import glob, json\n",
    "gt = json.load(open(\"../../mwe_data/synthetic_split_ff/ground_truth.json\"))\n",
    "ff = sorted(glob.glob(\"../../mwe_data/synthetic_split_ff/ff_step*.csv\"))\n",
    "s = gt[\"split_step\"]\n",
    "res_ff = analyzer.detect_splits(ff[s - 1], ff[s], d_tol=20.0, theta_tol_deg=15.0, angle_type=\"degrees\")\n",
    "print(res_ff[\"n_splits\"], res_ff[\"split_correspondences\"])  # matches gt['n_splitting_grains']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc21d46",
   "metadata": {},
   "source": [
    "## 4. Rare events: the grains that split the most\n",
    "\n",
    "Close the loop: rank grains by fragment count and flag the rare (most-fragmented) ones, reusing\n",
    "`rare_criteria_selection_library.select_highest_scalar` (the same REI selector used for\n",
    "nye/stress). Reorientation-from-initial (`FragmentationAnalyzer().write_reorientation_field` +\n",
    "the `abs_scalar_diff` metric) is the field-level analogue for full REI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3521c36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T23:28:14.917414Z",
     "iopub.status.busy": "2026-10-09T23:28:14.917255Z",
     "iopub.status.idle": "2026-10-09T23:28:14.926126Z",
     "shell.execute_reply": "2026-10-09T23:28:14.924800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most-fragmented grains: [4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "from graintrace import rare_criteria_selection_library as rcs\n",
    "rank = frags_df.rename(columns={\"grain_id\": \"cluster_label\", \"n_elements\": \"n\"})\n",
    "rare = rcs.select_highest_scalar(rank, required_cols=\"n_fragments\", k=3, min_size=1)\n",
    "print(\"most-fragmented grains:\", list(map(int, rare)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7f3177",
   "metadata": {},
   "source": [
    "See :doc:`/api/fragmentation`, :doc:`/api/ipf_orientation_tracking`, and\n",
    ":doc:`/api/plot_postprocessing` for the API, and\n",
    "`examples/demonstrate_reorientation_fragmentation.py` for the full runnable script."
   ]
  }
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