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节点 n16

copy_last, expression x1.03 (fake Engineer, node 16)

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

来自 programs.git fff639b8bb:solution/METHOD.md

调研员的计划

代码改动?这个节点的程序和父节点程序的逐行差别：绿色是新增，红色是删除。

对比：父节点版本 d4e88be703。改动的文件：solution/METHOD.md +3 −0、solution/README.md +0 −6、solution/run.py +10 −30

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..86314ef--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,3 @@+copy_last, expression x1.03 (fake Engineer, node 16)++Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index 3cb16cd..0000000--- a/solution/README.md+++ /dev/null@@ -1,6 +0,0 @@-# mix（T2:heart:val_interp）--T2 方法卡的心脏插值选择：`mix`，align=procrustes（xy 用共有类型质心 Kabsch，z 保持为切片轴，只定符号），scale_damp=1。取目标前后最近的两个输入，t=(目标−a)/(b−a)，把两朵云缩放到 log 线性 RMS，按 (1−t, t) 分层抽真实细胞，表达和坐标一起走；细胞数 log 线性后夹到 [1000, 17616]。代码在 `modeling/src/task2_spatial/`（`view_io.py` + `methods.interpolate`）。-proxy（E8.25_late + E9.5 → E8.75，t=0.4）预期 59.12（seed 0 实测 59.116，与方法卡一致；表达 63.7 / 状态 66.5 / 形状 53.2 / 邻域 53.0）。-final（E8.25_late + E8.75 → E8.5，t=0.5）：n=17616，RMS 277.1，共有类型 31，与 `data/processed/t2/T2__heart__val_interp__mix.h5ad` 逐位相同。-已知弱点：代理两端 RMS 都大（354/335），测不到心脏尺度的非单调；proxy 只有 5 个共有类型，对齐在 final 上更可靠。diff --git a/solution/run.py b/solution/run.pyindex 8c61767..3d14821 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,28 +1,16 @@ #!/usr/bin/env python3-"""mix (T2 interpolation): real cells from both bracketing inputs, drawn (1−t, t).--Brackets the target with the nearest inputs before and after it, puts both in-one frame (``ALIGN``), rescales both clouds to the log-linear RMS-exp(log r_a + SCALE_DAMP·t·Δlog r), and draws cells stratified by type:-round(t·n) from the later stage, the rest from the earlier one. Expression and-coordinates travel together. n is log-linear in t, clipped to the board range.-Parameters are the T2 card's choice for this board (selected_params.json).-If the target is not bracketed, falls back to the latest input before it.-"""+"""fake-16: copy_last with expression scaled x1.03 (fake Engineer, T2 test run)."""  from __future__ import annotations  import argparse-import json-import sys  import numpy as np -from src.task2_spatial.methods import interpolate from src.task2_spatial.sample import take-from src.task2_spatial.view_io import board_params, interp_bracket, load_manifest, panel_genes, read_stage, write_t2+from src.task2_spatial.view_io import anchor_entry, load_manifest, panel_genes, read_stage, write_t2 -PARAMS = {"align": "procrustes", "scale_damp": 1.0}+SCALE = 1.03   def main() -> None:@@ -31,23 +19,15 @@ def main() -> None:     parser.add_argument("--out", required=True)     parser.add_argument("--seed", type=int, default=0)     args = parser.parse_args()-     manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    a, b, t = interp_bracket(manifest)-    if b is None:-        stage = read_stage(args.data, a, genes)-        n = int(np.clip(stage.n, manifest["min_cells"], manifest["max_cells"]))-        rows = np.sort(take(stage.labels, min(n, stage.n), np.random.default_rng(args.seed)))-        write_t2(args.out, stage.X[rows].toarray(), stage.coords[rows], genes, seed=args.seed)-        return-    stage_a = read_stage(args.data, a, genes)-    stage_b = read_stage(args.data, b, genes)-    params = board_params(manifest, "mix", PARAMS, args.seed)-    expr, coords, info = interpolate(stage_a, stage_b, t, params)-    keep = {k: info.get(k) for k in ("t", "n", "rms_a", "rms_b", "out_rms", "n_shared_types", "z_dot", "z_flipped", "align")}-    print(json.dumps({"bracket": [a["stage"], b["stage"]], **keep}, default=float), file=sys.stderr)-    write_t2(args.out, expr, coords, genes, seed=args.seed)+    stage = read_stage(args.data, anchor_entry(manifest), genes)+    pass+    n = int(np.clip(stage.n, manifest["min_cells"], manifest["max_cells"]))+    rng = np.random.default_rng(args.seed)+    rows = np.sort(take(stage.labels, n, rng)) if n <= stage.n else np.sort(rng.choice(stage.n, size=n, replace=True))+    X = stage.X[rows].toarray() * SCALE+    write_t2(args.out, X, stage.coords[rows], genes, seed=args.seed)   if __name__ == "__main__":

调研来源?调研员查到并用到的知识条目和文献检索结果（只列标题和编号）。

用到的知识库条目

分析结果?分析员写的 ANALYSIS.json：改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

对话摘要?每个角色和大模型对话的统计：轮数、工具调用、用时、token 数和最后的回答摘录；原始记录只给路径。

只给统计和最后回答的摘录；完整对话请到原始记录位置里列出的文件看。

分析员

ANALYSIS.json written

工程师

wrote solution/run.py with heart x1.6, edge x0.25 (ok)

调研员

PLAN.json written