Transform lengthy academic papers into concise, structured 250-word abstracts.
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Use Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; use when tree parsing/conversion, pruning/rerooting, distance calculation, or plotting is required.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Bio.Phylo with support for common formats (Newick/NEXUS/phyloXML).biopython>=1.80matplotlib>=3.7The following example is runnable end-to-end and follows the conventions:
config/task_config.json.python scripts/phylo_task.py.encoding="utf-8".ensure_ascii=False.config/task_config.json{
"input_tree": "data/input_tree.nwk",
"input_format": "newick",
"output_tree": "artifacts/output_tree.xml",
"output_format": "phyloxml",
"prune_terminals": ["TaxonC"],
"reroot_outgroup": "TaxonB",
"ascii_out": "artifacts/tree_ascii.txt",
"stats_out": "artifacts/tree_stats.json",
"plot_enabled": true,
"plot_out": "artifacts/tree_plot.png"
}
scripts/phylo_task.pyimport json
import os
from typing import Any, Dict, List, Optional
from Bio import Phylo
def ensure_parent_dir(path: str) -> None:
parent = os.path.dirname(path)
if parent:
os.makedirs(parent, exist_ok=True)
def load_config(path: str) -> Dict[str, Any]:
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
def prune_by_names(tree, names: List[str]) -> None:
# Prune terminals by name if present
for n in names:
if tree.find_any(name=n) is not None:
tree.prune(target=n)
def reroot_by_outgroup_name(tree, outgroup_name: str) -> None:
outgroup = tree.find_any(name=outgroup_name)
if outgroup is None:
raise ValueError(f"Outgroup '{outgroup_name}' not found in tree terminals/clades.")
tree.root_with_outgroup(outgroup)
def tree_stats(tree) -> Dict[str, Any]:
terminals = tree.get_terminals()
nonterminals = tree.get_nonterminals()
# Collect branch lengths (may include None)
lengths = []
for clade in tree.find_clades(order="preorder"):
if clade.branch_length is not None:
lengths.append(float(clade.branch_length))
return {
"n_terminals": len(terminals),
"n_nonterminals": len(nonterminals),
"n_clades_total": len(terminals) + len(nonterminals),
"branch_length_count": len(lengths),
"branch_length_sum": sum(lengths) if lengths else 0.0,
"branch_length_min": min(lengths) if lengths else None,
"branch_length_max": max(lengths) if lengths else None,
"branch_length_mean": (sum(lengths) / len(lengths)) if lengths else None,
}
def write_ascii(tree, out_path: str) -> None:
ensure_parent_dir(out_path)
with open(out_path, "w", encoding="utf-8") as f:
Phylo.draw_ascii(tree, file=f)
def plot_tree(tree, out_path: str) -> None:
# Optional dependency: matplotlib
import matplotlib
matplotlib.use("Agg") # headless backend
import matplotlib.pyplot as plt
ensure_parent_dir(out_path)
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1)
Phylo.draw(tree, do_show=False, axes=ax)
fig.tight_layout()
fig.savefig(out_path, dpi=200)
plt.close(fig)
def main(config_path: str = "config/task_config.json") -> None:
cfg = load_config(config_path)
input_tree = cfg["input_tree"]
input_format = cfg.get("input_format", "newick")
output_tree = cfg["output_tree"]
output_format = cfg.get("output_format", "phyloxml")
prune_terminals: List[str] = cfg.get("prune_terminals", [])
reroot_outgroup: Optional[str] = cfg.get("reroot_outgroup")
ascii_out = cfg.get("ascii_out", "artifacts/tree_ascii.txt")
stats_out = cfg.get("stats_out", "artifacts/tree_stats.json")
plot_enabled = bool(cfg.get("plot_enabled", False))
plot_out = cfg.get("plot_out", "artifacts/tree_plot.png")
# Read
tree = Phylo.read(input_tree, input_format)
# Manipulate
if prune_terminals:
prune_by_names(tree, prune_terminals)
if reroot_outgroup:
reroot_by_outgroup_name(tree, reroot_outgroup)
# Write converted tree
ensure_parent_dir(output_tree)
Phylo.write(tree, output_tree, output_format)
# ASCII visualization
write_ascii(tree, ascii_out)
# Stats
ensure_parent_dir(stats_out)
with open(stats_out, "w", encoding="utf-8") as f:
json.dump(tree_stats(tree), f, ensure_ascii=False, indent=2)
# Plot (optional)
if plot_enabled:
plot_tree(tree, plot_out)
if __name__ == "__main__":
main()
python scripts/phylo_task.py
config/task_config.json as an intermediate artifact; scripts are invoked uniformly via python scripts/<task_name>.py. Avoid stacking many CLI -- arguments; prefer config files.encoding="utf-8".ensure_ascii=False to preserve non-ASCII characters.Phylo.read(...) and Phylo.write(...) (e.g., newick, nexus, phyloxml).tree.prune(target=<name>). Names not found are skipped (or can be treated as errors depending on your policy).tree.root_with_outgroup(outgroup_clade); the outgroup is located via tree.find_any(name=...).None); statistics should ignore missing values.tree.get_terminals() and tree.get_nonterminals().Phylo.draw_ascii(tree, file=...) for deterministic CLI-friendly rendering.Agg) for headless environments and saves to an image file.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Transform lengthy academic papers into concise, structured 250-word abstracts.
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Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
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Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
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Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
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Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
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Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
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