[workspace]
name = "bttoxin-pipeline"
channels = ["conda-forge", "bioconda"]
platforms = ["linux-64"]
version = "0.1.0"
channel-priority = "disabled"

# =========================
# digger 环境：bioconda 依赖
# =========================
[feature.digger]
channels = ["bioconda", "conda-forge", "bioconda/label/cf201901"]

[feature.digger.dependencies]
bttoxin_digger = "==1.0.10"
perl = "==5.26.2"
perl-file-tee = "==0.07"
perl-list-util = "==1.38"
blast = "==2.16.0"

# =========================
# pipeline 环境：Python 分析依赖
# =========================
[feature.pipeline.dependencies]
python = ">=3.9"
pandas = ">=2.0.0"
matplotlib = ">=3.7.0"
seaborn = ">=0.12.2"

# =========================
# frontend 环境：Node.js 前端依赖
# =========================
[feature.frontend.dependencies]
nodejs = ">=20"
pnpm = ">=10"

# =========================
# webbackend 环境：FastAPI 后端依赖
# =========================
[feature.webbackend.dependencies]
python = ">=3.11"
fastapi = "*"
uvicorn = "*"
sqlalchemy = "*"
psycopg2 = "*"
celery = "*"
redis-py = "*"
docker-py = "*"
pydantic = "*"
pydantic-settings = "*"
python-dotenv = "*"
httpx = "*"
pytest = "*"

# =========================
# CRISPR-Cas 环境：预留用于未来的 CRISPR-Cas 分析
# =========================
# 此环境预留用于 CRISPR-Cas 系统分析模块
# 实现计划：
# 1. 使用 CRISPRCasFinder 或 similar 工具识别 CRISPR arrays
# 2. 提取 spacer 序列并与毒素基因关联分析
# 3. 评估 CRISPR-Cas 系统对宿主防御的影响
#
# 预期依赖（待激活时添加）:
[feature.crispr.dependencies]
python = ">=3.10"
wget = "*"
curl = "*"
git = "*"
java-jdk = "*"
parallel = "*"
perl-app-cpanminus = "*"
hmmer = "*"
emboss = "*"
blast = "*"
perl-bioperl-core = "*"
perl-xml-simple = "*"
perl-digest-md5 = "*"
vmatch = "*"
muscle = "*"
prodigal = "*"
mamba = "*"
macsyfinder = "==2.1.2"

[feature.crispr.tasks]
install-casfinder = "macsydata install -u CASFinder==3.1.0"
detect = "python tools/crispr_cas_analysis/scripts/detect_crispr.py"
fusion = "python tools/crispr_cas_analysis/scripts/fusion_analysis.py"

# =========================
# 环境定义
# =========================
[environments]
digger = ["digger"]
pipeline = ["pipeline"]
frontend = ["frontend"]
webbackend = ["webbackend"]
crispr = ["crispr"]

# Actually, let's keep it simple. The tools are Python scripts. They can run in the 'pipeline' environment which has python and pandas.
# The 'crispr' feature defines dependencies.


# =========================
# pipeline tasks
# =========================
[feature.pipeline.tasks]
pipeline = "python scripts/run_single_fna_pipeline.py"
digger-only = "python scripts/run_digger_stage.py"
shoter = "python scripts/bttoxin_shoter.py"
plot = "python scripts/plot_shotter.py"

# =========================
# frontend tasks
# =========================
[feature.frontend.tasks]
fe-install = { cmd = "pnpm install", cwd = "frontend" }
fe-dev = { cmd = "pnpm dev --host", cwd = "frontend" }
fe-build = { cmd = "pnpm build", cwd = "frontend" }
fe-test = { cmd = "pnpm test:unit --run", cwd = "frontend" }
fe-lint = { cmd = "pnpm lint", cwd = "frontend" }

# =========================
# webbackend tasks
# =========================
[feature.webbackend.tasks]
api-dev = "uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000"
api-test = "pytest backend/ -v"
web-start = "bash scripts/start_web.sh"