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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000001",
+   "metadata": {},
+   "source": "# MMseqs2 CTS Demo\n\nRuns MMseqs2 `easy-cluster` on CDM genome files via the CDM Task Service.\n\n- **Image:** `ghcr.io/kbaseincubator/cdm_mmseqs2:0.1.0`\n- **Mode:** `easy-cluster`. All-vs-all clustering, no reference DB needed\n- **Cluster:** `kbase`\n- **Output:** `cts/io/jplfaria/output/mmseqs2/`\n\nSee [cdm_mmseqs2 repo](https://github.com/kbaseincubator/cdm_mmseqs2) for details."
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000002",
+   "metadata": {},
+   "source": "## 1. Setup clients"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000003",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "tscli = get_task_service_client()\nmincli = get_minio_client()\nprint(tscli.whoami())"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000004",
+   "metadata": {},
+   "source": "## 2. List input files\n\nUsing the shared test genome files already in MinIO with CRC64NVME checksums."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000005",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "objs = list(mincli.list_objects(\"cts\", prefix=\"io/gavin/test_files\", recursive=True))\ninput_files = [f\"cts/{o.object_name}\" for o in objs]\nprint(f\"{len(input_files)} input files:\")\nfor f in input_files:\n    print(f)"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000006",
+   "metadata": {},
+   "source": "## 3. Submit MMseqs2 easy-cluster job"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000007",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "IMAGE = \"ghcr.io/kbaseincubator/cdm_mmseqs2:0.1.0@sha256:24afa107c0dac1a6f093cc081ec19a577c4d0b156dfa8ee66bb2d3c41b97c082\"\nOUTPUT_DIR = \"cts/io/jplfaria/output/mmseqs2/test/v1\"\n\njob = tscli.submit_job(\n    IMAGE,\n    input_files,\n    OUTPUT_DIR,\n    cluster=\"kbase\",\n    declobber=True,\n    output_mount_point=\"/out\",\n    args=[\n        \"easy-cluster\",\n        tscli.insert_files(),\n        \"/out/cluster_results\",\n        \"/out/tmp\",\n        \"--threads\", \"4\",\n    ],\n    num_containers=1,\n    cpus=4,\n    memory=\"16GB\",\n    runtime=\"PT30M\"\n)\nprint(\"Job ID:\", job.id)"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000008",
+   "metadata": {},
+   "source": "## 4. Monitor job status"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000009",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "# Re-attach if notebook restarted:\n# job = tscli.get_job_by_id(\"PASTE_JOB_ID_HERE\")\n\nimport threading, json\nthread = threading.Thread(\n    target=lambda: print(json.dumps(job.wait_for_completion(), indent=4)),\n    daemon=True\n)\nthread.start()"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000010",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "print(job.get_job_status())"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000011",
+   "metadata": {},
+   "source": "## 5. Inspect output files"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000012",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "for o in job.get_job()[\"outputs\"]:\n    print(o[\"crc64nvme\"], o[\"file\"])"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4-0001-0001-0001-000000000013",
+   "metadata": {},
+   "source": "## 6. View cluster results\n\n`cluster_results_cluster.tsv` columns: `representative_id`, `member_id`"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1b2c3d4-0001-0001-0001-000000000014",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": "import io, pandas as pd\ncluster_obj = mincli.get_object(\"cts\", \"io/jplfaria/output/mmseqs2/test/v1/0/cluster_results_cluster.tsv\")\ndf = pd.read_csv(io.BytesIO(cluster_obj.read()), sep=\"\\t\", header=None, names=[\"representative\", \"member\"])\nprint(f\"{len(df)} cluster memberships, {df['representative'].nunique()} clusters\")\ndf.head(20)"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b684aa0-bf82-4d6b-b423-18206e32aaf4",
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": ""
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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