{ "cells": [ { "cell_type": "markdown", "id": "3b8c3776", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/casangi/graphviper/blob/main/docs/graph_building_tutorial_image.ipynb)" ] }, { "cell_type": "markdown", "id": "ed122f09-ead5-4a22-a70b-7702d556e7fe", "metadata": {}, "source": [ "# GraphVIPER Tutorial: Image" ] }, { "cell_type": "code", "execution_count": 1, "id": "e7850f0a-f46a-48cc-98b1-fde794af919a", "metadata": { "ExecuteTime": { "end_time": "2026-03-10T06:41:09.345459Z", "start_time": "2026-03-10T06:41:08.884462Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GraphVIPER version 0.0.39 already installed.\n" ] } ], "source": [ "import os\n", "import xradio\n", "\n", "from importlib.metadata import version\n", "\n", "try:\n", " import graphviper\n", "\n", " print(\"GraphVIPER version\", version(\"graphviper\"), \"already installed.\")\n", "except ImportError as e:\n", " print(e)\n", " print(\"Installing GraphVIPER\")\n", "\n", " os.system(\"pip install graphviper\")\n", "\n", " \n", "\n", " print(\"GraphVIPER version\", version(\"graphviper\"), \" installed.\")" ] }, { "cell_type": "markdown", "id": "e0aa8d3c-6fd9-474f-9223-6da8856ab948", "metadata": {}, "source": [ "## Download dataset" ] }, { "cell_type": "code", "execution_count": 2, "id": "d4f2b674-78e3-4dbb-9079-2b46825e1f3a", "metadata": { "ExecuteTime": { "end_time": "2026-03-10T06:41:11.581205Z", "start_time": "2026-03-10T06:41:11.540991Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-06 15:37:46,961\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Initializing download... \n" ] }, { "data": { "text/html": [ "
/Users/jsteeb/miniforge3/envs/zinc/lib/python3.13/site-packages/rich/live.py:260: UserWarning: install \"ipywidgets\"\n",
       "for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "
\n" ], "text/plain": [ "/Users/jsteeb/miniforge3/envs/zinc/lib/python3.13/site-packages/rich/live.py:260: UserWarning: install \"ipywidgets\"\n", "for Jupyter support\n", " warnings.warn('install \"ipywidgets\" for Jupyter support')\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import toolviper\n",
    "\n",
    "toolviper.utils.data.download(file=\"demo_simulated.im\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2682bb1c-c377-47f2-9faa-271108954d8f",
   "metadata": {},
   "source": [
    "## Setup Dask Cluster\n",
    "To simplify things we are going to start of by just using a single thread (everything will run in serial)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4e50179-3486-4454-9036-ff55d24346fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-10T06:41:13.810477Z",
     "start_time": "2026-03-10T06:41:13.789712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask\n",
    "\n",
    "dask.config.set(scheduler=\"synchronous\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001907e4-6ae7-4ab4-b763-a29639f8d2ea",
   "metadata": {},
   "source": [
    "## Inspect Image Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6dcf893-4411-4066-8f83-8d2cd7162827",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-10T06:41:19.939866Z",
     "start_time": "2026-03-10T06:41:17.050130Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful readonly open of default-locked table demo_simulated.im: 1 columns, 1 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 20MB\n",
       "Dimensions:              (time: 1, frequency: 50, polarization: 4, l: 200,\n",
       "                          m: 100, beam_params_label: 3)\n",
       "Coordinates:\n",
       "  * time                 (time) float64 8B 5.154e+04\n",
       "  * frequency            (frequency) float64 400B 1.415e+09 ... 1.415e+09\n",
       "    velocity             (frequency) float64 400B 1.146e+06 ... 1.136e+06\n",
       "  * polarization         (polarization) <U1 16B 'I' 'Q' 'U' 'V'\n",
       "  * l                    (l) float64 2kB 0.02909 0.0288 ... -0.02851 -0.0288\n",
       "  * m                    (m) float64 800B -0.01454 -0.01425 ... 0.01396 0.01425\n",
       "    right_ascension      (l, m) float64 160kB 0.0291 0.0291 ... 6.254 6.254\n",
       "    declination          (l, m) float64 160kB -0.01454 -0.01425 ... 0.01425\n",
       "  * beam_params_label    (beam_params_label) <U5 60B 'major' 'minor' 'pa'\n",
       "Data variables:\n",
       "    SKY                  (time, frequency, polarization, l, m) float32 16MB dask.array<chunksize=(1, 50, 4, 40, 20), meta=np.ndarray>\n",
       "    BEAM_FIT_PARAMS_SKY  (time, frequency, polarization, beam_params_label) float64 5kB dask.array<chunksize=(1, 50, 4, 3), meta=np.ndarray>\n",
       "    FLAG_SKY             (time, frequency, polarization, l, m) bool 4MB dask.array<chunksize=(1, 50, 4, 40, 20), meta=np.ndarray>\n",
       "Attributes:\n",
       "    coordinate_system_info:  {'reference_direction': {'attrs': {'frame': 'fk5...\n",
       "    type:                    image_dataset\n",
       "    data_groups:             {'base': {'sky': 'SKY', 'beam_fit_params_sky': '...
"
      ],
      "text/plain": [
       " Size: 20MB\n",
       "Dimensions:              (time: 1, frequency: 50, polarization: 4, l: 200,\n",
       "                          m: 100, beam_params_label: 3)\n",
       "Coordinates:\n",
       "  * time                 (time) float64 8B 5.154e+04\n",
       "  * frequency            (frequency) float64 400B 1.415e+09 ... 1.415e+09\n",
       "    velocity             (frequency) float64 400B 1.146e+06 ... 1.136e+06\n",
       "  * polarization         (polarization) \n",
       "    BEAM_FIT_PARAMS_SKY  (time, frequency, polarization, beam_params_label) float64 5kB dask.array\n",
       "    FLAG_SKY             (time, frequency, polarization, l, m) bool 4MB dask.array\n",
       "Attributes:\n",
       "    coordinate_system_info:  {'reference_direction': {'attrs': {'frame': 'fk5...\n",
       "    type:                    image_dataset\n",
       "    data_groups:             {'base': {'sky': 'SKY', 'beam_fit_params_sky': '..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xradio.image import load_image, open_image, write_image\n",
    "\n",
    "img_xds = open_image(store=\"demo_simulated.im\", chunks={\"l\": 40, \"m\": 20, \"freq\": 5})\n",
    "img_xds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e328c8-004f-4be9-ae2d-72c20e6e31da",
   "metadata": {},
   "source": [
    "## Create Parallel Coordinates, Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1278ba9b-9dcf-4a15-8265-b5e33c998d8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-10T06:41:26.793613Z",
     "start_time": "2026-03-10T06:41:26.423255Z"
    }
   },
   "outputs": [],
   "source": [
    "import dask\n",
    "\n",
    "from toolviper.utils.display import DataDict\n",
    "from graphviper.graph_tools.coordinate_utils import make_parallel_coord\n",
    "from graphviper.graph_tools.map import map\n",
    "\n",
    "from xradio.image import load_image\n",
    "\n",
    "\n",
    "input_parms = {}\n",
    "parallel_coords = {}\n",
    "parallel_coords[\"frequency\"] = make_parallel_coord(coord=img_xds.frequency, n_chunks=6)\n",
    "sel_parms = {}\n",
    "input_parms[\"input_data_store\"] = \"demo_simulated.im\"\n",
    "\n",
    "input_data = {\"img\": img_xds}\n",
    "\n",
    "from graphviper.graph_tools.coordinate_utils import (\n",
    "    interpolate_data_coords_onto_parallel_coords,\n",
    ")\n",
    "\n",
    "node_task_data_mapping = interpolate_data_coords_onto_parallel_coords(\n",
    "    parallel_coords, input_data\n",
    ")\n",
    "\n",
    "DataDict.html(node_task_data_mapping)\n",
    "\n",
    "\n",
    "def my_func(input_parms):\n",
    "    if input_parms[\"input_data\"] is None:\n",
    "        img_xds = load_image(\n",
    "            input_parms[\"input_data_store\"],\n",
    "            block_des=input_parms[\"data_selection\"][\"img\"],\n",
    "        )\n",
    "    else:\n",
    "        img_xds = input_parms[\"input_data\"][\"img\"]\n",
    "\n",
    "    toolviper.utils.display.DataDict.html(input_parms)\n",
    "\n",
    "    print(\"****\")\n",
    "\n",
    "\n",
    "graph = map(\n",
    "    input_data=input_data,\n",
    "    node_task_data_mapping=node_task_data_mapping,\n",
    "    node_task=my_func,\n",
    "    input_params=input_parms,\n",
    "    in_memory_compute=True,\n",
    ")\n",
    "dask_graph = dask.compute(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1854daed",
   "metadata": {},
   "source": [
    "## Data Loading Layer\n",
    "\n",
    "When the map partition size is smaller than the native on-disk zarr chunk\n",
    "size, each map task would otherwise decompress the same chunk independently.\n",
    "The **data loading layer** inserts shared *load nodes* that read each chunk\n",
    "once and distribute pre-cut slices to the map tasks:\n",
    "\n",
    "```\n",
    "zarr store  (e.g. 10 channels per compressed chunk)\n",
    "     │\n",
    " [load node]  ← decompresses once, shared across all tasks in the chunk\n",
    "    │   │\n",
    "[prepare] [prepare]  ← sub-selects each task's slice in memory\n",
    "    │         │\n",
    "[map task] [map task]  ← receives input_params[\"input_data\"], no disk I/O\n",
    "```\n",
    "\n",
    "### Detect native on-disk chunk sizes\n",
    "\n",
    "Open the image with native zarr chunking (`chunks={}`) so that the\n",
    "coordinate arrays carry dask chunk metadata, then call\n",
    "`get_disk_chunk_sizes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29d6b1d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful readonly open of default-locked table demo_simulated.im: 1 columns, 1 rows\n",
      "Native on-disk chunk sizes: {'frequency': 50}\n"
     ]
    }
   ],
   "source": [
    "from xradio.image import open_image\n",
    "from graphviper.graph_tools.coordinate_utils import (\n",
    "    make_parallel_coord,\n",
    "    interpolate_data_coords_onto_parallel_coords,\n",
    "    get_disk_chunk_sizes,\n",
    ")\n",
    "from graphviper.graph_tools.map import map\n",
    "from graphviper.graph_tools.generate_dask_workflow import generate_dask_workflow\n",
    "from graphviper.graph_tools import reduce\n",
    "\n",
    "# Open with native zarr chunks so get_disk_chunk_sizes can read chunk metadata.\n",
    "img_xds_native = open_image(store=\"demo_simulated.im\", chunks={})\n",
    "\n",
    "input_data = {\"img\": img_xds_native}\n",
    "\n",
    "# 10 map tasks across 50 frequency channels → 5 channels per task.\n",
    "parallel_coords = {\"frequency\": make_parallel_coord(coord=img_xds_native.frequency, n_chunks=10)}\n",
    "\n",
    "disk_chunk_sizes = get_disk_chunk_sizes(input_data, parallel_coords)\n",
    "print(\"Native on-disk chunk sizes:\", disk_chunk_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243d390f",
   "metadata": {},
   "source": [
    "### Define a load task for images\n",
    "\n",
    "The load task receives `load_params` with:\n",
    "- `\"input_data_store\"` — the zarr store path\n",
    "- `\"data_selection\"` — `{xds_name: {dim: slice}}` at disk-chunk granularity\n",
    "\n",
    "It must return `{xds_name: xarray.Dataset}` with the loaded disk chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22f7976a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image_chunk(load_params):\n",
    "    \"\"\"Load one native zarr chunk of the image.\"\"\"\n",
    "    from xradio.image import load_image\n",
    "\n",
    "    # data_selection[\"img\"] contains the disk-chunk-level slice dict.\n",
    "    return {\n",
    "        \"img\": load_image(\n",
    "            load_params[\"input_data_store\"],\n",
    "            block_des=load_params[\"data_selection\"][\"img\"],\n",
    "        )\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054f6b5e",
   "metadata": {},
   "source": [
    "### Map task with pre-loaded data\n",
    "\n",
    "When the load layer is active, `input_params[\"input_data\"]` is already\n",
    "populated with the sub-selected dataset — no disk read is needed inside\n",
    "the map task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e247fefd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load nodes : 1  (one per zarr chunk group)\n",
      "Map  nodes : 10  (one per parallel_coords chunk)\n",
      "Load → map assignments: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "def my_func(input_params):\n",
    "    # input_params[\"input_data\"] is set by the load layer.\n",
    "    img = input_params[\"input_data\"][\"img\"]\n",
    "    return float(img[\"SKY\"].values.sum())\n",
    "\n",
    "input_params = {\"input_data_store\": \"demo_simulated.im\"}\n",
    "\n",
    "node_task_data_mapping = interpolate_data_coords_onto_parallel_coords(\n",
    "    parallel_coords, input_data\n",
    ")\n",
    "\n",
    "viper_graph = map(\n",
    "    input_data=input_data,\n",
    "    node_task_data_mapping=node_task_data_mapping,\n",
    "    node_task=my_func,\n",
    "    input_params=input_params,\n",
    "    data_loading_task=load_image_chunk,\n",
    "    disk_chunk_sizes=disk_chunk_sizes,\n",
    ")\n",
    "\n",
    "n_load = len(viper_graph[\"load\"][\"input_params\"])\n",
    "n_map  = len(viper_graph[\"map\"][\"input_params\"])\n",
    "print(f\"Load nodes : {n_load}  (one per zarr chunk group)\")\n",
    "print(f\"Map  nodes : {n_map}  (one per parallel_coords chunk)\")\n",
    "print(f\"Load → map assignments: {viper_graph['map']['load_node_ids']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcea8ca2",
   "metadata": {},
   "source": [
    "### Visualise the graph\n",
    "\n",
    "The graph now has three layers: load → prepare → map.  Load nodes appear as\n",
    "shared ancestors of the prepare nodes that feed into each map task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b907fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       ""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viper_graph_reduce = reduce(viper_graph, lambda inputs, _: sum(inputs), {}, mode=\"tree\")\n",
    "dask_graph = generate_dask_workflow(viper_graph_reduce)\n",
    "dask.visualize(dask_graph, filename=\"image_map_graph_with_load_layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73c22674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful readonly open of default-locked table demo_simulated.im: 1 columns, 1 rows\n",
      "Total SKY sum across all frequency chunks: 678252.171875\n"
     ]
    }
   ],
   "source": [
    "result = dask.compute(dask_graph)[0]\n",
    "print(\"Total SKY sum across all frequency chunks:\", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ee7cef",
   "metadata": {},
   "source": [
    "### Scheduler plugin (distributed execution)\n",
    "\n",
    "For multi-worker distributed runs, register `ViperGraphPlugin` before\n",
    "submitting the graph.  It uses the `viper_load_group` and `viper_map_pair`\n",
    "annotations added automatically by `generate_dask_workflow` to:\n",
    "\n",
    "1. Process one zarr chunk group at a time → only one decompressed chunk\n",
    "   occupies worker memory at any moment.\n",
    "2. Schedule reduction-adjacent task pairs together → the binary tree\n",
    "   reduction progresses level-by-level with minimal intermediate results\n",
    "   in flight.\n",
    "\n",
    "```python\n",
    "from toolviper.dask.plugins.scheduler import ViperGraphPlugin\n",
    "from toolviper.dask.client import local_client\n",
    "\n",
    "client = local_client(cores=4, memory_limit=\"8GB\")\n",
    "client.register_scheduler_plugin(ViperGraphPlugin())\n",
    "# submit graph normally ...\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "zinc",
   "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}