hotwa
5883e13c56
- 新增 scripts/bttoxin_shoter.py：从 BPPRC 正样本 CSV 构建 name/亚家族/家族特异性索引， 解析 BtToxin_Digger All_Toxins.txt，计算 per-hit 权重并以 noisy-OR 合成菌株×目标目/物种分数， 输出 TSV/JSON；含 HMM 加成与配对毒素规则（Vip1/Vip2，Vpa/Vpb），other/unknown 桶。 - 新增端到端工具链： - scripts/run_single_fna_pipeline.py：Digger → Shotter → Plot → 打包 - scripts/plot_shotter.py：绘制热图并生成论文式/摘要式报告 - scripts/bttoxin_api.py 与 bttoxin/api.py：纯 Python API；bttoxin/cli.py 暴露 bttoxin-run - pyproject.toml：项目打包与 CLI 入口 - docs(README): 增加输入文件格式与结果解读，补充单目录写入方案 - chore(gitignore): 忽略 runs/ 与 tests/output - ci: 移除 .woodpecker/test.yml
544 lines
22 KiB
Python
544 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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Plotting utilities for Bttoxin_Shoter outputs.
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Generates heatmaps for:
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- Strain × Target Orders scores (from strain_target_scores.tsv)
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- Optional Per-hit contributions for one strain (from toxin_support.tsv)
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- Optional Strain × Target Species scores (from strain_target_species_scores.tsv)
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Can also write a Markdown report with:
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- summary mode: brief overview
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- paper mode: expanded, paper-like sections (Abstract, Methods, Results, Discussion, Params, Appendix)
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Dependencies:
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- pandas, matplotlib; seaborn is optional (for nicer heatmaps). If seaborn is
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missing, a pure-matplotlib fallback will be used.
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"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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from typing import List, Optional
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import pandas as pd
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try:
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import seaborn as sns # type: ignore
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_HAS_SNS = True
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except Exception:
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_HAS_SNS = False
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import matplotlib.pyplot as plt
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def _parse_figsize(s: str | None, default=(12, 6)):
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if not s:
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return default
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try:
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w, h = s.lower().split("x")
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return (float(w), float(h))
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except Exception:
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return default
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def _merge_unresolved_columns(df: pd.DataFrame, merge_unresolved: bool) -> pd.DataFrame:
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if not merge_unresolved:
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return df
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cols = df.columns.tolist()
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has_other = "other" in cols
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has_unknown = "unknown" in cols
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if has_other or has_unknown:
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df = df.copy()
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df["unresolved"] = df.get("other", 0.0) + df.get("unknown", 0.0)
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drop_cols = [c for c in ("other", "unknown") if c in df.columns]
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df = df.drop(columns=drop_cols)
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return df
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def plot_strain_scores(strain_scores_path: Path, out_path: Path, cmap: str, vmin: float, vmax: float, figsize: str | None, merge_unresolved: bool):
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df = pd.read_csv(strain_scores_path, sep="\t")
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base_cols = {"Strain", "TopOrder", "TopScore"}
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orders = [c for c in df.columns if c not in base_cols]
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mat = df.set_index("Strain")[orders]
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mat = _merge_unresolved_columns(mat, merge_unresolved)
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orders = list(mat.columns)
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# dynamic figure size if not provided
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if figsize:
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size = _parse_figsize(figsize)
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else:
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size = (max(8, 0.6 * len(orders) + 3), max(4, 0.35 * len(mat) + 2))
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plt.figure(figsize=size)
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if _HAS_SNS:
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ax = sns.heatmap(mat, cmap=cmap, vmin=vmin, vmax=vmax, cbar_kws={"label": "score"})
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else:
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ax = plt.gca()
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im = ax.imshow(mat.values, aspect="auto", cmap=cmap, vmin=vmin, vmax=vmax)
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plt.colorbar(im, ax=ax, label="score")
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ax.set_xticks(range(len(orders)))
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ax.set_xticklabels(orders, rotation=45, ha="right")
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ax.set_yticks(range(len(mat.index)))
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ax.set_yticklabels(mat.index)
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ax.set_title("Strain vs Target Orders (Shotter)")
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plt.tight_layout()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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plt.savefig(out_path, dpi=200)
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plt.close()
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def plot_per_hit_for_strain(toxin_support_path: Path, strain: str, out_path: Path, cmap: str, vmin: float, vmax: float, figsize: str | None, merge_unresolved: bool):
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hits = pd.read_csv(toxin_support_path, sep="\t")
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sub = hits[hits["Strain"].astype(str).eq(strain)].copy()
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if sub.empty:
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print(f"[plot_shotter] No hits for strain: {strain}")
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return
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base_cols = {
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"Strain","Protein_id","Hit_id","Identity","Aln_length","Hit_length","Coverage",
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"HMM","Family","NameKey","PartnerFulfilled","Weight","TopOrder","TopScore"
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}
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orders = [c for c in sub.columns if c not in base_cols]
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mat = sub.set_index("Hit_id")[orders]
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mat = _merge_unresolved_columns(mat, merge_unresolved)
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orders = list(mat.columns)
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# dynamic figure size if not provided
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if figsize:
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size = _parse_figsize(figsize)
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else:
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size = (max(8, 0.6 * len(orders) + 3), max(4, 0.35 * len(mat) + 2))
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plt.figure(figsize=size)
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if _HAS_SNS:
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ax = sns.heatmap(mat, cmap=cmap, vmin=vmin, vmax=vmax, cbar_kws={"label": "contrib"})
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else:
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ax = plt.gca()
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im = ax.imshow(mat.values, aspect="auto", cmap=cmap, vmin=vmin, vmax=vmax)
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plt.colorbar(im, ax=ax, label="contrib")
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ax.set_xticks(range(len(orders)))
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ax.set_xticklabels(orders, rotation=45, ha="right")
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ax.set_yticks(range(len(mat.index)))
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ax.set_yticklabels(mat.index)
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ax.set_title(f"Per-hit contributions for {strain}")
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plt.tight_layout()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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plt.savefig(out_path, dpi=200)
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plt.close()
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def plot_species_scores(species_scores_path: Path, out_path: Path, cmap: str, vmin: float, vmax: float, figsize: str | None):
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df = pd.read_csv(species_scores_path, sep="\t")
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species_cols = [c for c in df.columns if c not in ("Strain", "TopSpecies", "TopSpeciesScore")]
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if not species_cols:
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print("[plot_shotter] No species columns found; skip species heatmap")
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return
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mat = df.set_index("Strain")[species_cols]
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if figsize:
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size = _parse_figsize(figsize)
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else:
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size = (max(8, 0.6 * len(species_cols) + 3), max(4, 0.35 * len(mat) + 2))
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plt.figure(figsize=size)
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if _HAS_SNS:
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ax = sns.heatmap(mat, cmap=cmap, vmin=vmin, vmax=vmax, cbar_kws={"label": "score"})
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else:
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ax = plt.gca()
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im = ax.imshow(mat.values, aspect="auto", cmap=cmap, vmin=vmin, vmax=vmax)
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plt.colorbar(im, ax=ax, label="score")
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ax.set_xticks(range(len(species_cols)))
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ax.set_xticklabels(species_cols, rotation=45, ha="right")
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ax.set_yticks(range(len(mat.index)))
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ax.set_yticklabels(mat.index)
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ax.set_title("Strain vs Target Species (Shotter)")
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plt.tight_layout()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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plt.savefig(out_path, dpi=200)
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plt.close()
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def _percent(x: float) -> str:
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try:
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return f"{100.0*float(x):.1f}%"
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except Exception:
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return "NA"
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def _fmtf(x, nd=3):
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try:
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return f"{float(x):.{nd}f}"
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except Exception:
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return "NA"
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def _labels(lang: str):
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zh = {
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"title": "Bttoxin Shotter 论文式报告",
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"summary": "# Bttoxin Shotter Summary",
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"params": "## 参数",
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"abstract": "## 摘要",
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"methods": "## 数据与方法",
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"results": "## 结果",
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"discussion": "## 讨论与局限",
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"appendix": "## 附录",
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"top_targets": "## 各菌株 TopOrder/TopScore",
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"heat_orders": "## 热图：菌株 × 目标目",
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"heat_species": "## 热图：菌株 × 目标物种",
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"perhit": "## 命中 TopOrder/TopScore（前50）",
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"notes": "## 说明",
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"unresolved_ratio": "未解析贡献（other+unknown）整体占比",
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"order_distribution": "整体目标目分布（按合成分数求和排名前5）：",
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"hits_stats": "命中统计",
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"n_strains": "菌株数",
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"n_hits": "命中条数",
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"avg_weight": "平均权重",
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"table_header_strain": "Strain | TopOrder | TopScore",
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"table_header_hit": "Hit_id | Family | TopOrder | TopScore | Weight",
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"table_sep": "--- | --- | ---",
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"table_sep_hit": "--- | --- | --- | --- | ---",
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"methods_text": (
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"- 输入：BtToxin_Digger 的 All_Toxins.txt；BPPRC 特异性 CSV（activity=Yes）。\n"
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"- 特异性索引：按 name→亚家族→家族聚合，target_order/target_species 以加权和归一构成分布。\n"
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"- 相似性权重 w_hit：若 identity≥0.78 且 coverage≥0.8 则 base=1；若 0.45≤identity<0.78 则线性插值；否则 0。"
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"最终 w=min(1, base×coverage + 0.1×I(HMM))). 若需配对而未满足，w×=0.2。\n"
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"- 单命中贡献：c(order)=w_hit×P(order|·)。\n"
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"- 菌株层合成：noisy-OR，score(order)=1-∏(1-c_i(order))。\n"
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"- 物种维度：同上（若 CSV 含 target_species）。\n"
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"- 分类含义：other=家族可解析但无证据；unknown=家族不可解析。\n"
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),
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"discussion_text": (
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"- unresolved 偏高常见于：命名异常、家族稀缺、索引覆盖不足。\n"
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"- 可用更严格阈值或 --require_index_hit 减少伪阳性；也可 --merge_unresolved 合并展示。\n"
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"- 名称差异：Digger 与 BPPRC 均含 Cry 之外家族（Tpp/Spp/Vip 等），采用 name→亚家族→家族回退映射。\n"
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),
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"notes_items": [
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"TopOrder/TopScore：最高目标目及其分数（命中或菌株）。",
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"contrib：单命中对某目标目的贡献 w_hit×P(order|·)。",
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"unresolved=other+unknown，仅为可视化合并开关，不改变计算。",
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],
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}
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en = {
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"title": "Bttoxin Shotter Paper-style Report",
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"summary": "# Bttoxin Shotter Summary",
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"params": "## Parameters",
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"abstract": "## Abstract",
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"methods": "## Data and Methods",
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"results": "## Results",
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"discussion": "## Discussion and Limitations",
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"appendix": "## Appendix",
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"top_targets": "## Top targets per strain",
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"heat_orders": "## Heatmap: Strain × Orders",
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"heat_species": "## Heatmap: Strain × Species",
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"perhit": "## Per-hit TopOrder/TopScore (Top 50)",
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"notes": "## Notes",
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"unresolved_ratio": "Overall unresolved (other+unknown) fraction",
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"order_distribution": "Overall order distribution (top 5 by summed score):",
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"hits_stats": "Hit statistics",
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"n_strains": "#Strains",
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"n_hits": "#Hits",
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"avg_weight": "Avg weight",
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"table_header_strain": "Strain | TopOrder | TopScore",
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"table_header_hit": "Hit_id | Family | TopOrder | TopScore | Weight",
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"table_sep": "--- | --- | ---",
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"table_sep_hit": "--- | --- | --- | --- | ---",
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"methods_text": (
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"- Inputs: All_Toxins.txt from BtToxin_Digger; BPPRC specificity CSV (activity=Yes).\n"
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"- Specificity index: aggregate name→subfamily→family; build P(order/species|·) by weighted sums and normalization.\n"
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"- Similarity weight w_hit: base=1 if identity≥0.78 & coverage≥0.8; linear if 0.45≤identity<0.78; else 0.\n"
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" Final w=min(1, base×coverage + 0.1×I(HMM))). Partner missing → w×=0.2.\n"
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"- Per-hit contribution: c(order)=w_hit×P(order|·).\n"
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"- Strain level: noisy-OR, score(order)=1-∏(1-c_i(order)).\n"
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"- Species dimension: same if target_species present.\n"
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"- Buckets: other=parseable family but no evidence; unknown=unparseable family.\n"
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),
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"discussion_text": (
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"- High unresolved is common with unusual names, rare families, or limited index coverage.\n"
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"- Use stricter thresholds or --require_index_hit to reduce false positives; --merge_unresolved for visualization only.\n"
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"- Naming: both Digger and BPPRC include non-Cry families (Tpp/Spp/Vip etc.), mapped via name→subfamily→family backoff.\n"
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),
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"notes_items": [
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"TopOrder/TopScore: max target order and score (per hit or strain).",
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"contrib: per-hit contribution w_hit×P(order|·).",
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"unresolved=other+unknown merge is cosmetic; computation unchanged.",
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],
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}
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return zh if lang == "zh" else en
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def write_summary_md(
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out_path: Path,
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strain_scores_path: Path,
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toxin_support_path: Optional[Path],
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species_scores_path: Optional[Path],
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strain_heatmap_path: Optional[Path],
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per_hit_heatmap_path: Optional[Path],
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species_heatmap_path: Optional[Path],
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merge_unresolved: bool,
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args_namespace,
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):
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labels = _labels(getattr(args_namespace, "lang", "zh"))
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lines: List[str] = []
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lines.append(labels["summary"])
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lines.append("")
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# Parameters
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lines.append(labels["params"])
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lines.append(f"- allow_unknown_families: {getattr(args_namespace, 'allow_unknown_families', 'NA')}")
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lines.append(f"- require_index_hit: {getattr(args_namespace, 'require_index_hit', 'NA')}")
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lines.append(f"- min_identity: {getattr(args_namespace, 'min_identity', 'NA')}")
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lines.append(f"- min_coverage: {getattr(args_namespace, 'min_coverage', 'NA')}")
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lines.append(f"- merge_unresolved: {merge_unresolved}")
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lines.append("")
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# Top per strain table
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try:
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df = pd.read_csv(strain_scores_path, sep="\t")
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cols = [c for c in df.columns if c not in ("Strain", "TopOrder", "TopScore")]
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unresolved_cols = [c for c in ("other", "unknown") if c in cols]
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unresolved_total = df[unresolved_cols].sum(axis=1).sum() if unresolved_cols else 0.0
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total_sum = df[cols].sum(axis=1).sum() if cols else 0.0
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frac_unresolved = (unresolved_total / total_sum) if total_sum > 0 else 0.0
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lines.append(labels["top_targets"])
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lines.append("")
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lines.append(labels["table_header_strain"])
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lines.append(labels["table_sep"])
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for _, r in df.iterrows():
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lines.append(f"{r['Strain']} | {r.get('TopOrder','')} | {float(r.get('TopScore',0.0)):.3f}")
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lines.append("")
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lines.append(f"{labels['unresolved_ratio']}: {_percent(frac_unresolved)}")
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lines.append("")
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except Exception as e:
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lines.append(f"[warn] Failed to read strain scores: {e}")
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if strain_heatmap_path and strain_heatmap_path.exists():
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lines.append(labels["heat_orders"])
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lines.append(f"")
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lines.append("")
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if species_scores_path and species_scores_path.exists():
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lines.append(labels["heat_species"])
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if species_heatmap_path and species_heatmap_path.exists():
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lines.append(f"")
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else:
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lines.append("Species heatmap not generated.")
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lines.append("")
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# Per-hit summary
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if toxin_support_path and toxin_support_path.exists():
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try:
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hits = pd.read_csv(toxin_support_path, sep="\t")
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lines.append(labels["perhit"])
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lines.append("")
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lines.append(labels["table_header_hit"])
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lines.append(labels["table_sep_hit"])
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for _, r in hits.sort_values("TopScore", ascending=False).head(50).iterrows():
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lines.append(
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f"{r['Hit_id']} | {r.get('Family','')} | {r.get('TopOrder','')} | {float(r.get('TopScore',0.0)):.3f} | {float(r.get('Weight',0.0)):.3f}"
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)
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lines.append("")
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except Exception as e:
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lines.append(f"[warn] Failed to read toxin support: {e}")
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# FAQs
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lines.append(labels["notes"])
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for it in _labels(getattr(args_namespace, "lang", "zh"))["notes_items"]:
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lines.append(f"- {it}")
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text("\n".join(lines), encoding="utf-8")
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def write_report_md(
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out_path: Path,
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mode: str,
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lang: str,
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strain_scores_path: Path,
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toxin_support_path: Optional[Path],
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species_scores_path: Optional[Path],
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strain_heatmap_path: Optional[Path],
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per_hit_heatmap_path: Optional[Path],
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species_heatmap_path: Optional[Path],
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merge_unresolved: bool,
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args_namespace,
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):
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if mode == "summary":
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return write_summary_md(
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out_path,
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strain_scores_path,
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toxin_support_path,
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species_scores_path,
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strain_heatmap_path,
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per_hit_heatmap_path,
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species_heatmap_path,
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merge_unresolved,
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args_namespace,
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)
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L = _labels(lang)
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lines: List[str] = []
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lines.append(L["title"]) # title
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lines.append("")
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# Abstract: brief auto-summary
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try:
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df = pd.read_csv(strain_scores_path, sep="\t")
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base_cols = {"Strain", "TopOrder", "TopScore"}
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order_cols = [c for c in df.columns if c not in base_cols]
|
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orders_only = df[order_cols]
|
||
unresolved_cols = [c for c in ("other", "unknown") if c in order_cols]
|
||
frac_unres = 0.0
|
||
if order_cols:
|
||
tot = orders_only.sum(axis=1).sum()
|
||
unres = df[unresolved_cols].sum(axis=1).sum() if unresolved_cols else 0.0
|
||
frac_unres = (unres / tot) if tot > 0 else 0.0
|
||
top_global = []
|
||
if order_cols:
|
||
sums = orders_only.sum(axis=0).sort_values(ascending=False)
|
||
for k, v in sums.head(5).items():
|
||
if k in ("other", "unknown") and merge_unresolved:
|
||
continue
|
||
top_global.append(f"{k}:{_fmtf(v)}")
|
||
n_strains = len(df)
|
||
except Exception:
|
||
n_strains, frac_unres, top_global = 0, 0.0, []
|
||
|
||
n_hits, avg_w = 0, 0.0
|
||
try:
|
||
if toxin_support_path and toxin_support_path.exists():
|
||
hits = pd.read_csv(toxin_support_path, sep="\t")
|
||
n_hits = len(hits)
|
||
if "Weight" in hits.columns and len(hits) > 0:
|
||
avg_w = float(hits["Weight"].mean())
|
||
except Exception:
|
||
pass
|
||
|
||
lines.append(L["abstract"])
|
||
lines.append(
|
||
f"- {L['n_strains']}: {n_strains}; {L['n_hits']}: {n_hits}; {L['avg_weight']}: {_fmtf(avg_w)}."
|
||
)
|
||
if top_global:
|
||
lines.append(f"- {L['order_distribution']} " + ", ".join(top_global))
|
||
lines.append(f"- {L['unresolved_ratio']}: {_percent(frac_unres)}")
|
||
lines.append("")
|
||
|
||
# Methods
|
||
lines.append(L["methods"])
|
||
lines.append(L["methods_text"])
|
||
|
||
# Results
|
||
lines.append(L["results"])
|
||
try:
|
||
df = pd.read_csv(strain_scores_path, sep="\t")
|
||
lines.append(L["top_targets"])
|
||
lines.append("")
|
||
lines.append(L["table_header_strain"])
|
||
lines.append(L["table_sep"])
|
||
for _, r in df.iterrows():
|
||
lines.append(f"{r['Strain']} | {r.get('TopOrder','')} | {_fmtf(r.get('TopScore',0.0))}")
|
||
lines.append("")
|
||
except Exception as e:
|
||
lines.append(f"[warn] Failed to read strain scores: {e}")
|
||
|
||
if strain_heatmap_path and strain_heatmap_path.exists():
|
||
lines.append(L["heat_orders"])
|
||
lines.append(f"")
|
||
lines.append("")
|
||
if species_heatmap_path and species_heatmap_path.exists():
|
||
lines.append(L["heat_species"])
|
||
lines.append(f"")
|
||
lines.append("")
|
||
|
||
# Discussion
|
||
lines.append(L["discussion"])
|
||
lines.append(L["discussion_text"])
|
||
|
||
# Params
|
||
lines.append(L["params"])
|
||
lines.append(
|
||
f"- allow_unknown_families: {getattr(args_namespace, 'allow_unknown_families', 'NA')}\n"
|
||
f"- require_index_hit: {getattr(args_namespace, 'require_index_hit', 'NA')}\n"
|
||
f"- min_identity: {getattr(args_namespace, 'min_identity', 'NA')}\n"
|
||
f"- min_coverage: {getattr(args_namespace, 'min_coverage', 'NA')}\n"
|
||
f"- merge_unresolved: {merge_unresolved}"
|
||
)
|
||
lines.append("")
|
||
|
||
# Appendix
|
||
if toxin_support_path and toxin_support_path.exists():
|
||
try:
|
||
hits = pd.read_csv(toxin_support_path, sep="\t")
|
||
lines.append(L["appendix"])
|
||
lines.append(L["perhit"])
|
||
lines.append("")
|
||
lines.append(L["table_header_hit"])
|
||
lines.append(L["table_sep_hit"])
|
||
for _, r in hits.sort_values("TopScore", ascending=False).head(50).iterrows():
|
||
lines.append(
|
||
f"{r['Hit_id']} | {r.get('Family','')} | {r.get('TopOrder','')} | {_fmtf(r.get('TopScore',0.0))} | {_fmtf(r.get('Weight',0.0))}"
|
||
)
|
||
lines.append("")
|
||
except Exception as e:
|
||
lines.append(f"[warn] Failed to read toxin support: {e}")
|
||
|
||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||
out_path.write_text("\n".join(lines), encoding="utf-8")
|
||
|
||
|
||
def main():
|
||
ap = argparse.ArgumentParser(description="Plot heatmaps from Bttoxin_Shoter outputs")
|
||
ap.add_argument("--strain_scores", type=Path, default=Path("shotter_outputs/strain_target_scores.tsv"))
|
||
ap.add_argument("--toxin_support", type=Path, default=None)
|
||
ap.add_argument("--species_scores", type=Path, default=None)
|
||
ap.add_argument("--out_dir", type=Path, default=Path("shotter_outputs"))
|
||
ap.add_argument("--cmap", type=str, default="viridis")
|
||
ap.add_argument("--vmin", type=float, default=0.0)
|
||
ap.add_argument("--vmax", type=float, default=1.0)
|
||
ap.add_argument("--figsize", type=str, default=None, help="e.g. 12x6")
|
||
ap.add_argument("--per_hit_strain", type=str, default=None, help="If provided, also draw per-hit heatmap for this strain (requires --toxin_support)")
|
||
ap.add_argument("--merge_unresolved", action="store_true", default=False, help="Merge columns 'other' and 'unknown' into 'unresolved' for plots")
|
||
ap.add_argument("--summary_md", type=Path, default=None, help="Write a Markdown report to this path")
|
||
ap.add_argument("--report_mode", type=str, choices=["summary", "paper"], default="paper", help="Report template style")
|
||
ap.add_argument("--lang", type=str, choices=["zh", "en"], default="zh", help="Report language")
|
||
args = ap.parse_args()
|
||
|
||
args.out_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# Strain × Orders heatmap
|
||
out1 = args.out_dir / "strain_target_scores.png"
|
||
plot_strain_scores(args.strain_scores, out1, args.cmap, args.vmin, args.vmax, args.figsize, args.merge_unresolved)
|
||
print(f"Saved: {out1}")
|
||
|
||
# Optional per-hit heatmap
|
||
if args.per_hit_strain and args.toxin_support:
|
||
out2 = args.out_dir / f"per_hit_{args.per_hit_strain}.png"
|
||
plot_per_hit_for_strain(args.toxin_support, args.per_hit_strain, out2, args.cmap, args.vmin, args.vmax, args.figsize, args.merge_unresolved)
|
||
print(f"Saved: {out2}")
|
||
|
||
# Optional species heatmap
|
||
species_png: Optional[Path] = None
|
||
if args.species_scores and args.species_scores.exists():
|
||
species_png = args.out_dir / "strain_target_species_scores.png"
|
||
plot_species_scores(args.species_scores, species_png, args.cmap, args.vmin, args.vmax, args.figsize)
|
||
print(f"Saved: {species_png}")
|
||
|
||
# Report
|
||
default_name = "shotter_report_paper.md" if args.report_mode == "paper" else "shotter_summary.md"
|
||
summary_md = args.summary_md or (args.out_dir / default_name)
|
||
write_report_md(
|
||
out_path=summary_md,
|
||
mode=args.report_mode,
|
||
lang=args.lang,
|
||
strain_scores_path=args.strain_scores,
|
||
toxin_support_path=args.toxin_support,
|
||
species_scores_path=args.species_scores,
|
||
strain_heatmap_path=out1,
|
||
per_hit_heatmap_path=(args.out_dir / f"per_hit_{args.per_hit_strain}.png") if (args.per_hit_strain and args.toxin_support) else None,
|
||
species_heatmap_path=species_png,
|
||
merge_unresolved=args.merge_unresolved,
|
||
args_namespace=args,
|
||
)
|
||
print(f"Saved: {summary_md}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|