{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceType":"competition","sourceId":118765,"databundleVersionId":15231210,"isSourceIdPinned":false},{"sourceType":"datasetVersion","sourceId":14604295,"datasetId":9328538,"databundleVersionId":15440074},{"sourceType":"datasetVersion","sourceId":14962460,"datasetId":9577079,"databundleVersionId":15833819},{"sourceType":"datasetVersion","sourceId":14874339,"datasetId":9502242,"databundleVersionId":15736806},{"sourceType":"datasetVersion","sourceId":14962495,"datasetId":9577097,"databundleVersionId":15833858},{"sourceType":"datasetVersion","sourceId":10855324,"datasetId":6742586,"databundleVersionId":11219268}],"dockerImageVersionId":31287,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Reference\n\nhttps://www.kaggle.com/code/llkh0a/stanford-rna-3d-folding-part-2-protenix-tbm","metadata":{"_uuid":"d23dcac7-d3ef-4e96-88c4-2569364a467b","_cell_guid":"dd7996d2-36c0-4414-8824-0b2ebca11045","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}}},{"cell_type":"code","source":"!pip install --no-index --no-deps /kaggle/input/datasets/kami1976/biopython-cp312/biopython-1.86-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl\n\n!pip install --no-index --no-deps /kaggle/input/datasets/amirrezaaleyasin/biotite/biotite-1.6.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl\n\n!pip install --no-index --no-deps /kaggle/input/datasets/amirrezaaleyasin/rdkit-2025-9-5/rdkit-2025.9.5-cp312-cp312-manylinux_2_28_x86_64.whl","metadata":{"_uuid":"26c5957f-0c02-4a9f-96ca-c3413aa4c746","_cell_guid":"0e07bbf8-9478-4642-be72-4696c9fcafcc","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport sys\nimport pandas as pd\n\n# ── Local vs Kaggle mode ─────────────────────────────────────────────────────\n# On Kaggle competition rerun, KAGGLE_IS_COMPETITION_RERUN is set to a truthy value.\n# When running locally we do NOT exit — instead we cap the test set to a small\n# number of samples so the notebook finishes quickly.\n\nIS_KAGGLE = True # bool(os.environ.get(\"KAGGLE_IS_COMPETITION_RERUN\", \"\"))\n\n# How many test samples to use when running locally\nLOCAL_N_SAMPLES = None\n\nif IS_KAGGLE:\n    print(\"Running in KAGGLE COMPETITION mode — all test targets will be processed.\")\nelse:\n    print(f\"Running in LOCAL mode — only the first {LOCAL_N_SAMPLES} test targets \"\n          f\"will be processed to save time.\")","metadata":{"_uuid":"5ddf4d93-b20e-4d71-9771-3a1646b646fe","_cell_guid":"a5516eea-297a-4d32-ab47-ef06b353027b","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import gc\nimport json\nimport os\nimport time\n\nos.environ[\"LAYERNORM_TYPE\"] = \"torch\"\nos.environ.setdefault(\"RNA_MSA_DEPTH_LIMIT\", \"512\")\n\nimport sys\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom Bio.Align import PairwiseAligner\nfrom tqdm import tqdm","metadata":{"_uuid":"aab8b920-4712-4c51-9107-fdcd181df1b0","_cell_guid":"74bccd97-acd8-46f7-9e37-63a24ea4addc","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_c1_mask(data: dict, atom_array) -> torch.Tensor:\n    # 1. Try atom_array attributes first\n    if atom_array is not None:\n        try:\n            if hasattr(atom_array, \"centre_atom_mask\"):\n                m = atom_array.centre_atom_mask == 1\n                if hasattr(atom_array, \"is_rna\"):\n                    m = m & atom_array.is_rna\n                return torch.from_numpy(m).bool()\n            \n            if hasattr(atom_array, \"atom_name\"):\n                base = atom_array.atom_name == \"C1'\"\n                if hasattr(atom_array, \"is_rna\"):\n                    base = base & atom_array.is_rna\n                return torch.from_numpy(base).bool()\n        except Exception:\n            pass\n\n    # 2. Fallback to feature dict\n    f = data[\"input_feature_dict\"]\n    \n    if \"centre_atom_mask\" in f:\n        return (f[\"centre_atom_mask\"] == 1).bool()\n    if \"center_atom_mask\" in f:\n        return (f[\"center_atom_mask\"] == 1).bool()\n        \n    # Heuristic fallback: check which index gives us roughly N_token atoms\n    n_tokens = data.get(\"N_token\", torch.tensor(0)).item()\n    mask11 = (f[\"atom_to_tokatom_idx\"] == 11).bool()\n    mask12 = (f[\"atom_to_tokatom_idx\"] == 12).bool()\n    \n    c11 = mask11.sum().item()\n    c12 = mask12.sum().item()\n    \n    # Return the one closer to N_tokens (likely one per residue)\n    if abs(c11 - n_tokens) < abs(c12 - n_tokens):\n        return mask11\n    else:\n        return mask12","metadata":{"_uuid":"89d16ff8-8de3-48b4-b988-c72b3680390f","_cell_guid":"07bfdc7e-f7bd-4b66-b408-32e2ea347499","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n# ─────────────── Paths & Constants ───────────────────────────────────────────\nDATA_BASE              = \"/kaggle/input/stanford-rna-3d-folding-2\"\nDEFAULT_TEST_CSV       = f\"{DATA_BASE}/test_sequences.csv\"\nDEFAULT_TRAIN_CSV      = f\"{DATA_BASE}/train_sequences.csv\"\nDEFAULT_TRAIN_LBLS     = f\"{DATA_BASE}/train_labels.csv\"\nDEFAULT_VAL_CSV        = f\"{DATA_BASE}/validation_sequences.csv\"\nDEFAULT_VAL_LBLS       = f\"{DATA_BASE}/validation_labels.csv\"\nDEFAULT_OUTPUT         = \"/kaggle/working/submission.csv\"\n\nDEFAULT_CODE_DIR = (\n    \"/kaggle/input/datasets/qiweiyin/protenix-v1-adjusted\"\n    \"/Protenix-v1-adjust-v2/Protenix-v1-adjust-v2/Protenix-v1\"\n)\nDEFAULT_ROOT_DIR = DEFAULT_CODE_DIR\n\nMODEL_NAME    = \"protenix_base_20250630_v1.0.0\"\nN_SAMPLE      = 5\nSEED          = 42\nMAX_SEQ_LEN   = int(os.environ.get(\"MAX_SEQ_LEN\",   \"512\"))\nCHUNK_OVERLAP = int(os.environ.get(\"CHUNK_OVERLAP\",  \"128\"))\n\n# TBM quality thresholds — sequences below these get routed to Protenix\nMIN_SIMILARITY       = float(os.environ.get(\"MIN_SIMILARITY\",       \"0.0\"))\nMIN_PERCENT_IDENTITY = float(os.environ.get(\"MIN_PERCENT_IDENTITY\", \"50.0\"))\n\n# Set False to skip Protenix and use de-novo fallback instead\nUSE_PROTENIX = True\n\n\ndef parse_bool(value: str, default: bool = False) -> str:\n    v = str(value).strip().lower()\n    if v in {\"1\", \"true\", \"t\", \"yes\", \"y\", \"on\"}:\n        return \"true\"\n    if v in {\"0\", \"false\", \"f\", \"no\", \"n\", \"off\"}:\n        return \"false\"\n    return \"true\" if default else \"false\"\n\n\nUSE_MSA      = parse_bool(os.environ.get(\"USE_MSA\",      \"false\"))\nUSE_TEMPLATE = parse_bool(os.environ.get(\"USE_TEMPLATE\", \"false\"))\nUSE_RNA_MSA  = parse_bool(os.environ.get(\"USE_RNA_MSA\",  \"true\"))\n\nMODEL_N_SAMPLE = int(os.environ.get(\"MODEL_N_SAMPLE\", str(N_SAMPLE)))\n\n\n# ─────────────── General Utilities ───────────────────────────────────────────\ndef seed_everything(seed: int) -> None:\n    os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    np.random.seed(seed)\n    torch.backends.cudnn.benchmark = False\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.enabled = True\n    torch.use_deterministic_algorithms(True)\n\n\ndef resolve_paths():\n    test_csv   = os.environ.get(\"TEST_CSV\",           DEFAULT_TEST_CSV)\n    output_csv = os.environ.get(\"SUBMISSION_CSV\",     DEFAULT_OUTPUT)\n    code_dir   = os.environ.get(\"PROTENIX_CODE_DIR\",  DEFAULT_CODE_DIR)\n    root_dir   = os.environ.get(\"PROTENIX_ROOT_DIR\",  DEFAULT_ROOT_DIR)\n    return test_csv, output_csv, code_dir, root_dir\n\n\ndef ensure_required_files(root_dir: str) -> None:\n    for p, name in [\n        (Path(root_dir) / \"checkpoint\" / f\"{MODEL_NAME}.pt\",          \"checkpoint\"),\n        (Path(root_dir) / \"common\" / \"components.cif\",                \"CCD file\"),\n        (Path(root_dir) / \"common\" / \"components.cif.rdkit_mol.pkl\",  \"CCD cache\"),\n    ]:\n        if not p.exists():\n            raise FileNotFoundError(f\"Missing {name}: {p}\")\n\n\n# ─────────────── Protenix Input / Config Helpers ─────────────────────────────\ndef build_input_json(df: pd.DataFrame, json_path: str) -> None:\n    data = [\n        {\n            \"name\": row[\"target_id\"],\n            \"covalent_bonds\": [],\n            \"sequences\": [{\"rnaSequence\": {\"sequence\": row[\"sequence\"], \"count\": 1}}],\n        }\n        for _, row in df.iterrows()\n    ]\n    with open(json_path, \"w\", encoding=\"utf-8\") as f:\n        json.dump(data, f)\n\n\ndef build_configs(input_json_path: str, dump_dir: str, model_name: str):\n    from configs.configs_base import configs as configs_base\n    from configs.configs_data import data_configs\n    from configs.configs_inference import inference_configs\n    from configs.configs_model_type import model_configs\n    from protenix.config.config import parse_configs\n\n    base = {**configs_base, **{\"data\": data_configs}, **inference_configs}\n\n    def deep_update(t, p):\n        for k, v in p.items():\n            if isinstance(v, dict) and k in t and isinstance(t[k], dict):\n                deep_update(t[k], v)\n            else:\n                t[k] = v\n\n    deep_update(base, model_configs[model_name])\n    arg_str = \" \".join([\n        f\"--model_name {model_name}\",\n        f\"--input_json_path {input_json_path}\",\n        f\"--dump_dir {dump_dir}\",\n        f\"--use_msa {USE_MSA}\",\n        f\"--use_template {USE_TEMPLATE}\",\n        f\"--use_rna_msa {USE_RNA_MSA}\",\n        f\"--sample_diffusion.N_sample {MODEL_N_SAMPLE}\",\n        f\"--seeds {SEED}\",\n    ])\n    return parse_configs(configs=base, arg_str=arg_str, fill_required_with_null=True)\n\n\ndef get_c1_mask(data: dict, atom_array) -> torch.Tensor:\n    # 1. Try atom_array attributes first\n    if atom_array is not None:\n        try:\n            if hasattr(atom_array, \"centre_atom_mask\"):\n                m = atom_array.centre_atom_mask == 1\n                if hasattr(atom_array, \"is_rna\"):\n                    m = m & atom_array.is_rna\n                return torch.from_numpy(m).bool()\n            \n            if hasattr(atom_array, \"atom_name\"):\n                base = atom_array.atom_name == \"C1'\"\n                if hasattr(atom_array, \"is_rna\"):\n                    base = base & atom_array.is_rna\n                return torch.from_numpy(base).bool()\n        except Exception:\n            pass\n\n    # 2. Fallback to feature dict\n    f = data[\"input_feature_dict\"]\n    \n    # CASE A: center_atom_mask exists\n    if \"center_atom_mask\" in f:\n        return (f[\"center_atom_mask\"] == 1).bool()\n    if \"centre_atom_mask\" in f:\n        return (f[\"centre_atom_mask\"] == 1).bool()\n        \n    # CASE B: Use atom_name\n    if \"atom_name\" in f:\n        # Check against \"C1'\" (byte encoded or string?)\n        # For now assume typical behavior is center_atom_mask is present.\n        pass\n\n    # CASE C: atom_to_tokatom_idx fallback\n    # The index for C1' is typically 11 or 12 depending on featurizer.\n    # Let's try to match exactly C1' if possible.\n    # But usually 'centre_atom_mask' should be there.\n    \n    # If we fall through, assume standard mask\n    return (f[\"atom_to_tokatom_idx\"] == 11).bool()\n\n\ndef get_feature_c1_mask(data: dict) -> torch.Tensor:\n    f = data[\"input_feature_dict\"]\n    if \"centre_atom_mask\" in f:\n        return f[\"centre_atom_mask\"].long() == 1\n    return f[\"atom_to_tokatom_idx\"].long() == 12\n\n\ndef coords_to_rows(target_id: str, seq: str, coords: np.ndarray) -> list:\n    \"\"\"coords shape: (N_SAMPLE, seq_len, 3)\"\"\"\n    rows = []\n    for i in range(len(seq)):\n        row = {\"ID\": f\"{target_id}_{i + 1}\", \"resname\": seq[i], \"resid\": i + 1}\n        for s in range(N_SAMPLE):\n            if s < coords.shape[0] and i < coords.shape[1]:\n                x, y, z = coords[s, i]\n            else:\n                x, y, z = 0.0, 0.0, 0.0\n            row[f\"x_{s + 1}\"] = float(x)\n            row[f\"y_{s + 1}\"] = float(y)\n            row[f\"z_{s + 1}\"] = float(z)\n        rows.append(row)\n    return rows\n\n\ndef pad_samples(coords: np.ndarray, n: int) -> np.ndarray:\n    if coords.shape[0] >= n:\n        return coords[:n]\n    if coords.shape[0] == 0:\n        return np.zeros((n, coords.shape[1], 3), dtype=coords.dtype)\n    extra = np.repeat(coords[:1], n - coords.shape[0], axis=0)\n    return np.concatenate([coords, extra], axis=0)\n\n\ndef split_into_chunks(seq_len: int, max_len: int, overlap: int) -> list:\n    \"\"\"Split a sequence into overlapping (start, end) chunks.\"\"\"\n    if seq_len <= max_len:\n        return [(0, seq_len)]\n    chunks = []\n    step = max_len - overlap\n    pos = 0\n    while pos < seq_len:\n        end = min(pos + max_len, seq_len)\n        chunks.append((pos, end))\n        if end == seq_len:\n            break\n        pos += step\n    return chunks\n\n\ndef kabsch_align(P: np.ndarray, Q: np.ndarray):\n    \"\"\"Compute optimal rotation R and translation t so that  R @ P + t ≈ Q.\"\"\"\n    centroid_P = P.mean(axis=0)\n    centroid_Q = Q.mean(axis=0)\n    Pc = P - centroid_P\n    Qc = Q - centroid_Q\n    H = Pc.T @ Qc\n    U, _, Vt = np.linalg.svd(H)\n    d = np.linalg.det(Vt.T @ U.T)\n    S = np.eye(3)\n    if d < 0:\n        S[2, 2] = -1\n    R = Vt.T @ S @ U.T\n    t = centroid_Q - R @ centroid_P\n    return R, t\n\n\ndef stitch_chunk_coords(chunk_coords_list: list,\n                        chunk_ranges: list,\n                        seq_len: int) -> np.ndarray:\n    \"\"\"\n    Merge overlapping chunk coordinates into a full sequence geometry.\n    Applies Kabsch alignment on overlapping residues, and smoothly\n    blends the coordinates using a linear weight ramp.\n    \"\"\"\n    if len(chunk_coords_list) == 1:\n        coords = chunk_coords_list[0]\n        if coords.shape[0] >= seq_len:\n            return coords[:seq_len]\n        out = np.zeros((seq_len, 3), dtype=coords.dtype)\n        out[:coords.shape[0]] = coords\n        return out\n\n    # Start with the first chunk aligned to itself (identity)\n    aligned = [chunk_coords_list[0].copy()]\n\n    for i in range(1, len(chunk_coords_list)):\n        prev_start, prev_end = chunk_ranges[i - 1]\n        cur_start, cur_end = chunk_ranges[i]\n\n        ov_start = cur_start\n        ov_end = min(prev_end, cur_end)\n        ov_len = ov_end - ov_start\n\n        if ov_len < 3:\n            # Cannot align reliably, just trust the coordinates as-is\n            aligned.append(chunk_coords_list[i].copy())\n            continue\n\n        prev_ov = aligned[i - 1][ov_start - prev_start: ov_end - prev_start]\n        cur_ov = chunk_coords_list[i][ov_start - cur_start: ov_end - cur_start]\n\n        # Ignore invalid residues (e.g. padding/blank)\n        valid = ~(np.isnan(prev_ov).any(axis=1) | np.isnan(cur_ov).any(axis=1))\n        if valid.sum() < 3:\n            aligned.append(chunk_coords_list[i].copy())\n            continue\n\n        # Align current chunk to previous chunk using only the overlap region\n        R, t = kabsch_align(cur_ov[valid], prev_ov[valid])\n        transformed = (chunk_coords_list[i] @ R.T) + t\n        aligned.append(transformed)\n\n    # Blend them together\n    full = np.zeros((seq_len, 3), dtype=np.float64)\n    weights = np.zeros(seq_len, dtype=np.float64)\n\n    for i, ((s, e), coords) in enumerate(zip(chunk_ranges, aligned)):\n        chunk_len = coords.shape[0]\n        actual_end = min(s + chunk_len, seq_len)\n        used_len = actual_end - s\n\n        w = np.ones(used_len, dtype=np.float64)\n\n        if i > 0:\n            ov_start = s\n            ov_end = min(chunk_ranges[i - 1][1], e)\n            ramp_len = ov_end - ov_start\n            if ramp_len > 0:\n                w[:ramp_len] = np.linspace(0.0, 1.0, ramp_len)\n\n        if i < len(chunk_ranges) - 1:\n            next_s = chunk_ranges[i + 1][0]\n            ramp_start = next_s - s\n            ramp_len = actual_end - next_s\n            if ramp_len > 0 and ramp_start < used_len:\n                w[ramp_start:used_len] = np.linspace(1.0, 0.0, ramp_len)\n\n        full[s:actual_end] += coords[:used_len] * w[:, None]\n        weights[s:actual_end] += w\n\n    mask = weights > 0\n    full[mask] /= weights[mask, None]\n\n    return full\n\n\n# ─────────────── TBM Core Functions ──────────────────────────────────────────\ndef _make_aligner(seq_len: int = None) -> PairwiseAligner:\n    al = PairwiseAligner()\n    al.mode                           = \"global\"\n    al.match_score                    = 2\n    al.mismatch_score                 = -1.5\n    \n    open_gap = -8.0\n    extend_gap = -0.4\n    \n    if seq_len is not None:\n        length_factor = max(0.5, min(2.0, 1.0 + (seq_len - 100) / 1000.0))\n        open_gap *= length_factor\n        extend_gap *= length_factor\n        \n    al.open_gap_score                 = open_gap\n    al.extend_gap_score               = extend_gap\n    al.query_left_open_gap_score      = open_gap\n    al.query_left_extend_gap_score    = extend_gap\n    al.query_right_open_gap_score     = open_gap\n    al.query_right_extend_gap_score   = extend_gap\n    al.target_left_open_gap_score     = open_gap\n    al.target_left_extend_gap_score   = extend_gap\n    al.target_right_open_gap_score    = open_gap\n    al.target_right_extend_gap_score  = extend_gap\n    return al\n\n\n_aligner = _make_aligner()\n\n\ndef parse_stoichiometry(stoich: str) -> list:\n    if pd.isna(stoich) or str(stoich).strip() == \"\":\n        return []\n    return [(ch.strip(), int(cnt)) for part in str(stoich).split(\";\")\n            for ch, cnt in [part.split(\":\")]]\n\n\ndef parse_fasta(fasta_content: str) -> dict:\n    out, cur, parts = {}, None, []\n    for line in str(fasta_content).splitlines():\n        line = line.strip()\n        if not line:\n            continue\n        if line.startswith(\">\"):\n            if cur is not None:\n                out[cur] = \"\".join(parts)\n            cur = line[1:].split()[0]\n            parts = []\n        else:\n            parts.append(line.replace(\" \", \"\"))\n    if cur is not None:\n        out[cur] = \"\".join(parts)\n    return out\n\n\ndef get_chain_segments(row) -> list:\n    seq    = row[\"sequence\"]\n    stoich = row.get(\"stoichiometry\", \"\")\n    all_sq = row.get(\"all_sequences\", \"\")\n    if (pd.isna(stoich) or pd.isna(all_sq)\n            or str(stoich).strip() == \"\" or str(all_sq).strip() == \"\"):\n        return [(0, len(seq))]\n    try:\n        chain_dict = parse_fasta(all_sq)\n        order = parse_stoichiometry(stoich)\n        segs, pos = [], 0\n        for ch, cnt in order:\n            base = chain_dict.get(ch)\n            if base is None:\n                return [(0, len(seq))]\n            for _ in range(cnt):\n                segs.append((pos, pos + len(base)))\n                pos += len(base)\n        return segs if pos == len(seq) else [(0, len(seq))]\n    except Exception:\n        return [(0, len(seq))]\n\n\ndef build_segments_map(df: pd.DataFrame) -> tuple:\n    seg_map, stoich_map = {}, {}\n    for _, r in df.iterrows():\n        tid               = r[\"target_id\"]\n        seg_map[tid]      = get_chain_segments(r)\n        raw_s             = r.get(\"stoichiometry\", \"\")\n        stoich_map[tid]   = \"\" if pd.isna(raw_s) else str(raw_s)\n    return seg_map, stoich_map\n\n\ndef process_labels(labels_df: pd.DataFrame) -> dict:\n    coords = {}\n    prefixes = labels_df[\"ID\"].str.rsplit(\"_\", n=1).str[0]\n    for prefix, grp in labels_df.groupby(prefixes):\n        coords[prefix] = grp.sort_values(\"resid\")[[\"x_1\", \"y_1\", \"z_1\"]].values\n    return coords\n\n\ndef _build_aligned_strings(query_seq, template_seq, alignment):\n    q_segs, t_segs = alignment.aligned\n    aq, at, qi, ti = [], [], 0, 0\n    for (qs, qe), (ts, te) in zip(q_segs, t_segs):\n        while qi < qs: aq.append(query_seq[qi]);    at.append(\"-\");              qi += 1\n        while ti < ts: aq.append(\"-\");              at.append(template_seq[ti]); ti += 1\n        for qp, tp in zip(range(qs, qe), range(ts, te)):\n            aq.append(query_seq[qp]); at.append(template_seq[tp])\n        qi, ti = qe, te\n    while qi < len(query_seq):    aq.append(query_seq[qi]);    at.append(\"-\");              qi += 1\n    while ti < len(template_seq): aq.append(\"-\");              at.append(template_seq[ti]); ti += 1\n    return \"\".join(aq), \"\".join(at)\n\n\ndef find_similar_sequences_detailed(query_seq, train_seqs_df, train_coords_dict, top_n=30):\n    results = []\n    for _, row in train_seqs_df.iterrows():\n        tid, tseq = row[\"target_id\"], row[\"sequence\"]\n        if tid not in train_coords_dict:\n            continue\n        if abs(len(tseq) - len(query_seq)) / max(len(tseq), len(query_seq)) > 0.3:\n            continue\n        local_aligner = _make_aligner(min(len(query_seq), len(tseq)))\n        aln       = next(iter(local_aligner.align(query_seq, tseq)))\n        norm_s    = aln.score / (2 * min(len(query_seq), len(tseq)))\n        identical = sum(\n            1 for (qs, qe), (ts, te) in zip(*aln.aligned)\n            for qp, tp in zip(range(qs, qe), range(ts, te))\n            if query_seq[qp] == tseq[tp]\n        )\n        pct_id = 100 * identical / len(query_seq)\n        aq, at = _build_aligned_strings(query_seq, tseq, aln)\n        results.append((tid, tseq, norm_s, train_coords_dict[tid], pct_id, aq, at))\n    results.sort(key=lambda x: x[2], reverse=True)\n    return results[:top_n]\n\n\ndef adapt_template_to_query(query_seq, template_seq, template_coords) -> np.ndarray:\n    local_aligner = _make_aligner(min(len(query_seq), len(template_seq)))\n    aln        = next(iter(local_aligner.align(query_seq, template_seq)))\n    new_coords = np.full((len(query_seq), 3), np.nan)\n    for (qs, qe), (ts, te) in zip(*aln.aligned):\n        chunk = template_coords[ts:te]\n        if len(chunk) == (qe - qs):\n            new_coords[qs:qe] = chunk\n    for i in range(len(new_coords)):\n        if np.isnan(new_coords[i, 0]):\n            pv = next((j for j in range(i - 1, -1, -1) if not np.isnan(new_coords[j, 0])), -1)\n            nv = next((j for j in range(i + 1, len(new_coords)) if not np.isnan(new_coords[j, 0])), -1)\n            if pv >= 0 and nv >= 0:\n                w = (i - pv) / (nv - pv)\n                new_coords[i] = (1 - w) * new_coords[pv] + w * new_coords[nv]\n            elif pv >= 0:\n                new_coords[i] = new_coords[pv] + [3, 0, 0]\n            elif nv >= 0:\n                new_coords[i] = new_coords[nv] + [3, 0, 0]\n            else:\n                new_coords[i] = [i * 3, 0, 0]\n    return np.nan_to_num(new_coords)\n\n\ndef adaptive_rna_constraints(coords, target_id, segments_map, confidence=1.0, passes=2) -> np.ndarray:\n    X        = coords.copy()\n    segments = segments_map.get(target_id, [(0, len(X))])\n    strength = max(0.75 * (1.0 - min(confidence, 0.97)), 0.02)\n    for _ in range(passes):\n        for s, e in segments:\n            C = X[s:e]; L = e - s\n            if L < 3:\n                continue\n            # bond i–i+1  ~5.95 Å\n            d    = C[1:] - C[:-1]; dist = np.linalg.norm(d, axis=1) + 1e-6\n            adj  = d * ((5.95 - dist) / dist)[:, None] * (0.22 * strength)\n            C[:-1] -= adj; C[1:] += adj\n            # soft i–i+2  ~10.2 Å\n            d2   = C[2:] - C[:-2]; d2n = np.linalg.norm(d2, axis=1) + 1e-6\n            adj2 = d2 * ((10.2 - d2n) / d2n)[:, None] * (0.10 * strength)\n            C[:-2] -= adj2; C[2:] += adj2\n            # Laplacian smoothing\n            C[1:-1] += (0.06 * strength) * (0.5 * (C[:-2] + C[2:]) - C[1:-1])\n            # self-avoidance\n            if L >= 25:\n                idx  = np.linspace(0, L - 1, min(L, 160)).astype(int) if L > 220 else np.arange(L)\n                P    = C[idx]; diff = P[:, None, :] - P[None, :, :]\n                dm   = np.linalg.norm(diff, axis=2) + 1e-6\n                sep  = np.abs(idx[:, None] - idx[None, :])\n                mask = (sep > 2) & (dm < 3.2)\n                if np.any(mask):\n                    vec = (diff * ((3.2 - dm) / dm)[:, :, None] * mask[:, :, None]).sum(axis=1)\n                    C[idx] += (0.015 * strength) * vec\n            X[s:e] = C\n    return X\n\n\ndef _rotmat(axis, ang):\n    a = np.asarray(axis, float); a /= np.linalg.norm(a) + 1e-12\n    x, y, z = a; c, s = np.cos(ang), np.sin(ang); CC = 1 - c\n    return np.array([[c+x*x*CC, x*y*CC-z*s, x*z*CC+y*s],\n                     [y*x*CC+z*s, c+y*y*CC, y*z*CC-x*s],\n                     [z*x*CC-y*s, z*y*CC+x*s, c+z*z*CC]])\n\n\ndef apply_hinge(coords, seg, rng, deg=22, confidence=1.0):\n    s, e = seg; L = e - s\n    if L < 30: return coords\n    deg_scaled = deg * max(0.2, min(2.0, 1.5 - confidence))\n    pivot = s + int(rng.integers(10, L - 10))\n    R = _rotmat(rng.normal(size=3), np.deg2rad(float(rng.uniform(-deg_scaled, deg_scaled))))\n    X = coords.copy(); p0 = X[pivot].copy()\n    X[pivot+1:e] = (X[pivot+1:e] - p0) @ R.T + p0\n    return X\n\n\ndef jitter_chains(coords, segs, rng, deg=12, trans=1.5, confidence=1.0):\n    X = coords.copy(); gc_ = X.mean(0, keepdims=True)\n    scale = max(0.2, min(2.0, 1.5 - confidence))\n    deg_scaled = deg * scale\n    trans_scaled = trans * scale\n    for s, e in segs:\n        R     = _rotmat(rng.normal(size=3), np.deg2rad(float(rng.uniform(-deg_scaled, deg_scaled))))\n        shift = rng.normal(size=3); shift = shift / (np.linalg.norm(shift) + 1e-12) * float(rng.uniform(0, trans_scaled))\n        c     = X[s:e].mean(0, keepdims=True)\n        X[s:e] = (X[s:e] - c) @ R.T + c + shift\n    X -= X.mean(0, keepdims=True) - gc_\n    return X\n\n\ndef smooth_wiggle(coords, segs, rng, amp=0.8, confidence=1.0):\n    X = coords.copy()\n    scale = max(0.2, min(2.0, 1.5 - confidence))\n    amp_scaled = amp * scale\n    for s, e in segs:\n        L = e - s\n        if L < 20: continue\n        ctrl = np.linspace(0, L - 1, 6); disp = rng.normal(0, amp_scaled, (6, 3)); t = np.arange(L)\n        X[s:e] += np.vstack([np.interp(t, ctrl, disp[:, k]) for k in range(3)]).T\n    return X\n\n\ndef generate_rna_structure(sequence: str, seed=None) -> np.ndarray:\n    \"\"\"Idealized A-form RNA helix — last-resort de-novo fallback.\"\"\"\n    if seed is not None:\n        np.random.seed(seed)\n    n = len(sequence); coords = np.zeros((n, 3))\n    for i in range(n):\n        ang = i * 0.6\n        coords[i] = [10.0 * np.cos(ang), 10.0 * np.sin(ang), i * 2.5]\n    return coords\n\n\n# ─────────────── TBM Phase ───────────────────────────────────────────────────\ndef tbm_phase(test_df, train_seqs_df, train_coords_dict, segments_map):\n    \"\"\"\n    Phase 1 — Template-Based Modeling.\n\n    Returns\n    -------\n    template_predictions : {target_id: [np.ndarray(seq_len, 3), ...]}\n        0 to N_SAMPLE predictions per target, from real templates.\n    protenix_queue : {target_id: (n_needed, full_sequence)}\n        Targets that still need more predictions.\n    \"\"\"\n    print(f\"\\n{'='*60}\")\n    print(f\"PHASE 1: Template-Based Modeling\")\n    print(f\"  MIN_SIMILARITY = {MIN_SIMILARITY}  |  MIN_PCT_IDENTITY = {MIN_PERCENT_IDENTITY}\")\n    print(f\"{'='*60}\")\n    t0 = time.time()\n\n    template_predictions: dict = {}\n    protenix_queue:       dict = {}\n\n    for _, row in test_df.iterrows():\n        tid = row[\"target_id\"]\n        seq = row[\"sequence\"]\n        segs = segments_map.get(tid, [(0, len(seq))])\n\n        similar = find_similar_sequences_detailed(seq, train_seqs_df, train_coords_dict, top_n=30)\n        preds   = []\n        used    = set()\n\n        for i, (tmpl_id, tmpl_seq, sim, tmpl_coords, pct_id, _, _) in enumerate(similar):\n            if len(preds) >= N_SAMPLE:\n                break\n            if sim < MIN_SIMILARITY or pct_id < MIN_PERCENT_IDENTITY:\n                break           # list is sorted by sim, so no point continuing\n            if tmpl_id in used:\n                continue\n\n            rng     = np.random.default_rng((row.name * 10000000000 + i * 10007) % (2**32))\n            adapted = adapt_template_to_query(seq, tmpl_seq, tmpl_coords)\n\n            # Diversity transforms (same strategy as the 0-409 TBM notebook)\n            slot = len(preds)\n            if slot == 0:\n                X = adapted\n            elif slot == 1:\n                X = adapted + rng.normal(0, max(0.01, (0.40 - sim) * 0.06), adapted.shape)\n            elif slot == 2:\n                longest = max(segs, key=lambda se: se[1] - se[0])\n                X = apply_hinge(adapted, longest, rng, confidence=sim)\n            elif slot == 3:\n                X = jitter_chains(adapted, segs, rng, confidence=sim)\n            else:\n                X = smooth_wiggle(adapted, segs, rng, confidence=sim)\n\n            refined = adaptive_rna_constraints(X, tid, segments_map, confidence=sim)\n            preds.append(refined)\n            used.add(tmpl_id)\n\n        template_predictions[tid] = preds\n        n_needed = N_SAMPLE - len(preds)\n        if n_needed > 0:\n            protenix_queue[tid] = (n_needed, seq)\n            print(f\"  {tid} ({len(seq)} nt): {len(preds)} TBM → need {n_needed} from Protenix\")\n        else:\n            print(f\"  {tid} ({len(seq)} nt): all {N_SAMPLE} from TBM ✓\")\n\n    elapsed = time.time() - t0\n    n_full  = len(test_df) - len(protenix_queue)\n    print(f\"\\nPhase 1 done in {elapsed:.1f}s\")\n    print(f\"  Fully covered by TBM : {n_full}\")\n    print(f\"  Need Protenix        : {len(protenix_queue)}\")\n    return template_predictions, protenix_queue\n\n\n# ─────────────── Main ────────────────────────────────────────────────────────\ndef main() -> None:\n    test_csv, output_csv, code_dir, root_dir = resolve_paths()\n\n    if not os.path.isdir(code_dir):\n        raise FileNotFoundError(\n            f\"Missing PROTENIX_CODE_DIR: {code_dir}. \"\n            \"Set PROTENIX_CODE_DIR to the repo path.\"\n        )\n\n    os.environ[\"PROTENIX_ROOT_DIR\"] = root_dir\n    sys.path.append(code_dir)\n    ensure_required_files(root_dir)\n    seed_everything(SEED)\n\n    # ── Load test data ──────────────────────────────────────────────────────\n    test_df_full = pd.read_csv(test_csv)\n    test_df      = (test_df_full.head(LOCAL_N_SAMPLES) if not IS_KAGGLE\n                    else test_df_full).reset_index(drop=True)\n    print(f\"Test targets : {len(test_df)}\"\n          + (\" (LOCAL MODE)\" if not IS_KAGGLE else \"\"))\n\n    seq_by_id = dict(zip(test_df[\"target_id\"], test_df[\"sequence\"]))\n\n    # Truncated copy for Protenix (Protenix has token limits)\n    test_df_trunc = test_df.copy()\n    test_df_trunc[\"sequence\"] = test_df_trunc[\"sequence\"].str[:MAX_SEQ_LEN]\n\n    # ── Load training data for TBM ──────────────────────────────────────────\n    print(\"\\nLoading training data for TBM …\")\n    train_seqs   = pd.read_csv(DEFAULT_TRAIN_CSV)\n    val_seqs     = pd.read_csv(DEFAULT_VAL_CSV)\n    train_labels = pd.read_csv(DEFAULT_TRAIN_LBLS)\n    val_labels   = pd.read_csv(DEFAULT_VAL_LBLS)\n\n    combined_seqs   = pd.concat([train_seqs,   val_seqs],    ignore_index=True)\n    combined_labels = pd.concat([train_labels, val_labels],  ignore_index=True)\n    train_coords    = process_labels(combined_labels)\n    segments_map, _ = build_segments_map(test_df)\n\n    print(f\"Template pool: {len(combined_seqs)} sequences, {len(train_coords)} structures\")\n\n    # ─── PHASE 1: TBM ──────────────────────────────────────────────────────\n    template_preds, protenix_queue = tbm_phase(\n        test_df, combined_seqs, train_coords, segments_map\n    )\n\n    # ─── PHASE 2: Protenix (only for targets that need extra predictions) ──\n    protenix_preds: dict = {}   # target_id -> np.ndarray (n_needed, seq_len, 3)\n\n    if protenix_queue and USE_PROTENIX:\n        print(f\"\\n{'='*60}\")\n        print(f\"PHASE 2: Protenix for {len(protenix_queue)} targets\")\n        print(f\"{'='*60}\")\n\n        work_dir = Path(\"/kaggle/working\")\n        work_dir.mkdir(parents=True, exist_ok=True)\n\n        # ── 1. Preparation: create tasks for all sequences/chunks ────────────\n        tasks = []          # list of dict for input_json\n        chunk_info = {}     # target_id -> list of {\"name\": chunk_name, \"range\": (s, e)}\n        \n        for target_id, (n_needed, full_seq) in protenix_queue.items():\n            seq_len = len(full_seq)\n            if seq_len <= MAX_SEQ_LEN:\n                tasks.append({\"target_id\": target_id, \"sequence\": full_seq})\n                chunk_info[target_id] = [{\"name\": target_id, \"range\": (0, seq_len)}]\n                print(f\"  {target_id} ({seq_len} nt): single pass queued\")\n            else:\n                chunks = split_into_chunks(seq_len, MAX_SEQ_LEN, CHUNK_OVERLAP)\n                print(f\"  {target_id} ({seq_len} nt): {len(chunks)} chunks queued \"\n                      f\"{[(s, e) for s, e in chunks]}\")\n                \n                chunk_info[target_id] = []\n                for ci, (cs, ce) in enumerate(chunks):\n                    chunk_name = f\"{target_id}_chunk{ci}\"\n                    sub_seq = full_seq[cs:ce]\n                    tasks.append({\"target_id\": chunk_name, \"sequence\": sub_seq})\n                    chunk_info[target_id].append({\"name\": chunk_name, \"range\": (cs, ce)})\n\n        # Build combined input JSON\n        tasks_df = pd.DataFrame(tasks)\n        input_json_path = str(work_dir / \"protenix_queue_input.json\")\n        build_input_json(tasks_df, input_json_path)\n\n        from protenix.data.inference.infer_dataloader import InferenceDataset\n        from runner.inference import (InferenceRunner,\n                                      update_gpu_compatible_configs,\n                                      update_inference_configs)\n\n        # Initialize model exactly ONCE\n        configs = build_configs(input_json_path, str(work_dir / \"outputs\"), MODEL_NAME)\n        configs = update_gpu_compatible_configs(configs)\n        runner  = InferenceRunner(configs)\n        dataset = InferenceDataset(configs)\n\n        # ── 2. Inference: process dataset and collect predictions ────────────\n        raw_predictions = {}  # sample_name -> coords (np.ndarray or None)\n\n        def _extract_c1_coords(prediction, feat, chunk_seq_len, raw_coords):\n            if \"centre_atom_mask\" in feat:\n                mask = (feat[\"centre_atom_mask\"] == 1).to(raw_coords.device)\n            elif \"center_atom_mask\" in feat:\n                mask = (feat[\"center_atom_mask\"] == 1).to(raw_coords.device)\n            elif \"atom_to_tokatom_idx\" in feat:\n                m11 = (feat[\"atom_to_tokatom_idx\"] == 11).to(raw_coords.device)\n                m12 = (feat[\"atom_to_tokatom_idx\"] == 12).to(raw_coords.device)\n                c11, c12 = m11.sum(), m12.sum()\n                mask = m11 if abs(c11 - chunk_seq_len) < abs(c12 - chunk_seq_len) else m12\n            else:\n                mask = torch.zeros(raw_coords.shape[1], dtype=torch.bool, device=raw_coords.device)\n            \n            coords = raw_coords[:, mask, :].detach().cpu().numpy()\n            \n            # Collapse check\n            if coords.shape[1] > 1:\n                diffs = np.linalg.norm(coords[0, 1:] - coords[0, :-1], axis=-1)\n                if np.all(diffs < 1e-4):\n                    print(f\"    WARNING: Collapsed coordinates detected\")\n                    return None\n            \n            if coords.shape[1] != chunk_seq_len:\n                if coords.shape[1] == 1 and chunk_seq_len > 1:\n                    return None\n                padded = np.zeros((coords.shape[0], chunk_seq_len, 3), dtype=np.float32)\n                ml = min(coords.shape[1], chunk_seq_len)\n                padded[:, :ml, :] = coords[:, :ml, :]\n                coords = padded\n            return coords\n\n        for i in tqdm(range(len(dataset)), desc=\"Protenix Inference\"):\n            data, atom_array, err = dataset[i]\n            sample_name = data.get(\"sample_name\", f\"sample_{i}\")\n            \n            if err:\n                print(f\"  {sample_name} data error: {err}\")\n                raw_predictions[sample_name] = None\n                del data, atom_array, err\n                gc.collect(); torch.cuda.empty_cache(); gc.collect()\n                continue\n            \n            # Find how many samples are needed for this specific query\n            target_id = sample_name.split(\"_chunk\")[0] if \"_chunk\" in sample_name else sample_name\n            n_needed = protenix_queue.get(target_id, (N_SAMPLE, \"\"))[0]\n            sub_seq_len = data[\"N_token\"].item() # roughly correct\n            \n            try:\n                new_cfg = update_inference_configs(configs, sub_seq_len)\n                new_cfg.sample_diffusion.N_sample = n_needed\n                runner.update_model_configs(new_cfg)\n                \n                pred = runner.predict(data)\n                raw_coords = pred[\"coordinate\"]\n                \n                coords = _extract_c1_coords(pred, data[\"input_feature_dict\"], \n                                            sub_seq_len, raw_coords)\n                raw_predictions[sample_name] = coords\n            except Exception as exc:\n                print(f\"  {sample_name} inference failed: {exc}\")\n                import traceback; traceback.print_exc()\n                raw_predictions[sample_name] = None\n            finally:\n                try: del pred, data, atom_array, raw_coords\n                except: pass\n                gc.collect(); torch.cuda.empty_cache(); gc.collect()\n\n        # ── 3. Post-processing: Stitching and final formatting ───────────────\n        for target_id, (n_needed, full_seq) in protenix_queue.items():\n            seq_len = len(full_seq)\n            chunks = chunk_info.get(target_id, [])\n            \n            if not chunks:\n                continue\n\n            if len(chunks) == 1:\n                # Single pass\n                coords = raw_predictions.get(target_id)\n                protenix_preds[target_id] = coords\n                if coords is not None:\n                    print(f\"  {target_id}: {coords.shape[0]} predictions generated\")\n                else:\n                    print(f\"  {target_id}: FAILED\")\n            else:\n                # Stitch chunks together\n                chunk_results_per_sample = {s: [] for s in range(n_needed)}\n                all_ok = True\n                \n                for ci, cinfo in enumerate(chunks):\n                    cname = cinfo[\"name\"]\n                    crange = cinfo[\"range\"]\n                    ccoords = raw_predictions.get(cname)\n                    \n                    if ccoords is None:\n                        all_ok = False\n                        break\n                    \n                    for s_idx in range(n_needed):\n                        if s_idx < ccoords.shape[0]:\n                            chunk_results_per_sample[s_idx].append((ccoords[s_idx], crange))\n                        else:\n                            chunk_results_per_sample[s_idx].append((ccoords[-1], crange))\n                \n                if not all_ok:\n                    print(f\"  {target_id}: chunked inference incomplete, using fallback\")\n                    protenix_preds[target_id] = None\n                    continue\n                \n                stitched_samples = []\n                for s_idx in range(n_needed):\n                    items = chunk_results_per_sample[s_idx]\n                    coords_list = [c for c, _ in items]\n                    ranges_list = [r for _, r in items]\n                    full_coords = stitch_chunk_coords(coords_list, ranges_list, seq_len)\n                    stitched_samples.append(full_coords)\n                \n                result = np.stack(stitched_samples, axis=0)\n                protenix_preds[target_id] = result\n                print(f\"  {target_id}: {result.shape[0]} stitched predictions generated\")\n# ...existing code...\n\n    elif protenix_queue and not USE_PROTENIX:\n        print(f\"\\nPHASE 2 skipped (USE_PROTENIX=False). \"\n              f\"De-novo fallback will cover {len(protenix_queue)} targets.\")\n\n    # ─── PHASE 3: Combine everything ───────────────────────────────────────\n    print(f\"\\n{'='*60}\")\n    print(\"PHASE 3: Combine TBM + Protenix + de-novo fallback\")\n    print(f\"{'='*60}\")\n\n    all_rows = []\n\n    for _, row in test_df.iterrows():\n        tid = row[\"target_id\"]\n        seq = row[\"sequence\"]\n\n        combined: list = list(template_preds.get(tid, []))  # TBM predictions\n\n        # Append Protenix predictions to fill remaining slots\n        ptx = protenix_preds.get(tid)\n        if ptx is not None and ptx.ndim == 3:\n            for j in range(ptx.shape[0]):\n                if len(combined) >= N_SAMPLE:\n                    break\n                combined.append(ptx[j])  # (seq_len, 3)\n\n        # De-novo fallback for any still-empty slots\n        n_denovo = 0\n        while len(combined) < N_SAMPLE:\n            seed_val = row.name * 1000000 + len(combined) * 1000\n            dn       = generate_rna_structure(seq, seed=seed_val)\n            combined.append(adaptive_rna_constraints(dn, tid, segments_map, confidence=0.2))\n            n_denovo += 1\n\n        if n_denovo:\n            print(f\"  {tid}: {n_denovo} slot(s) filled with de-novo fallback\")\n\n        # Stack to (N_SAMPLE, seq_len, 3) and write rows\n        stacked = np.stack(combined[:N_SAMPLE], axis=0)\n        all_rows.extend(coords_to_rows(tid, seq, stacked))\n\n    # ── Save ───────────────────────────────────────────────────────────────\n    sub = pd.DataFrame(all_rows)\n    cols = [\"ID\", \"resname\", \"resid\"] + [\n        f\"{c}_{i}\" for i in range(1, N_SAMPLE + 1) for c in [\"x\", \"y\", \"z\"]\n    ]\n    coord_cols = [c for c in cols if c.startswith((\"x_\", \"y_\", \"z_\"))]\n    sub[coord_cols] = sub[coord_cols].clip(-999.999, 9999.999)\n    sub[cols].to_csv(output_csv, index=False)\n\n    print(f\"\\n✓ Saved submission to {output_csv}  ({len(sub):,} rows)\")","metadata":{"_uuid":"cc6a1510-0849-4225-bcbc-36f6086fedd4","_cell_guid":"77d87796-d1f3-450b-be42-ea79317e8921","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Main","metadata":{"_uuid":"3adf17b4-fd95-4092-affd-bc02644f4415","_cell_guid":"fb87bc2f-99ff-4d78-80f2-9d181c70ee4b","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}}},{"cell_type":"code","source":"\nif __name__ == \"__main__\":\n    main()","metadata":{"_uuid":"2d291ec4-9099-4e8e-9860-9ff9245f9d92","_cell_guid":"81b18830-babd-473d-880e-2912c41362e3","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#read submission.csv\nsubmission_path = \"/kaggle/working/submission.csv\"\nsubmission_df = pd.read_csv(submission_path)\nprint(submission_df.head(20))","metadata":{"_uuid":"729b2d51-9c70-4305-a00c-23a9d68eb3e2","_cell_guid":"d34fb5ce-6443-438c-9aed-f26e82a791e0","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null}]}