Changed setup.py to pyproject.toml

example/tensorqtl_examples.ipynb

@@ -28,7 +28,7 @@
      "output_type": "stream",
      "text": [
       "torch: 2.5.1+cu124 (CUDA 12.4), device: cuda\n",
-      "pandas 2.2.3\n"
+      "pandas: 2.2.3\n"
      ]
     }
    ],
@@ -83,7 +83,7 @@
       "  * Computing associations\n",
       "    Mapping chromosome chr18\n",
       "    processing phenotype 301/301\n",
-      "    time elapsed: 0.05 min\n",
+      "    time elapsed: 0.04 min\n",
       "    * writing output\n",
       "done.\n"
      ]

pyproject.toml

@@ -0,0 +1,37 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["tensorqtl"]
+
+[project]
+name = "tensorqtl"
+version = "1.0.10"
+dependencies = [
+    "numpy",
+    "pandas",
+    "Pgenlib>=0.90.1",
+    "qtl",
+    "scipy",
+    "torch",
+]
+authors = [
+    {name = "Francois Aguet", email = "[email protected]"}
+]
+maintainers = [
+    {name = "Francois Aguet", email = "[email protected]"}
+]
+description = "GPU-accelerated QTL mapper"
+readme = "README.md"
+license = {file = "LICENSE"}
+keywords = ["Quantitative trait loci"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Programming Language :: Python :: 3",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+]
+
+[project.urls]
+Repository = "https://github.com/broadinstitute/tensorqtl.git"

tensorqtl/__init__.py

@@ -1,2 +1,4 @@
-__version__ = "1.0.10"
+import importlib.metadata
 from .tensorqtl import *
+
+__version__ = importlib.metadata.version(__name__)

tensorqtl/tensorqtl.py

@@ -9,11 +9,11 @@
 import pickle
 import argparse
 from collections import defaultdict
+import importlib.metadata
 
 sys.path.insert(1, os.path.dirname(__file__))
 from core import *
 from post import *
-from . import __version__
 import genotypeio, cis, trans, susie
 
 
@@ -59,7 +59,7 @@ def main():
         raise ValueError("Interactions are only supported in 'cis_nominal' or 'trans' mode.")
 
     logger = SimpleLogger(os.path.join(args.output_dir, f'{args.prefix}.tensorQTL.{args.mode}.log'))
-    logger.write(f'[{datetime.now().strftime("%b %d %H:%M:%S")}] Running TensorQTL v{__version__}: {args.mode.split("_")[0]}-QTL mapping')
+    logger.write(f'[{datetime.now().strftime("%b %d %H:%M:%S")}] Running TensorQTL v{importlib.metadata.version("tensorqtl")}: {args.mode.split("_")[0]}-QTL mapping')
     if torch.cuda.is_available():
         logger.write(f'  * using GPU ({torch.cuda.get_device_name(torch.cuda.current_device())})')
     else:
@@ -230,14 +230,14 @@ def main():
         summary_df = pd.read_csv(args.cis_output, sep='\t', index_col=0)
         summary_df.rename(columns={'minor_allele_samples':'ma_samples', 'minor_allele_count':'ma_count'}, inplace=True)
         if args.chunk_size is None:
-            res_df = cis.map_independent(genotype_df, variant_df, summary_df, phenotype_df, phenotype_pos_df, covariates_df,
+            res_df = cis.map_independent(genotype_df, variant_df, summary_df, phenotype_df, phenotype_pos_df, covariates_df=covariates_df,
                                          group_s=group_s, fdr=args.fdr, nperm=args.permutations, window=args.window,
                                          maf_threshold=maf_threshold, logger=logger, seed=args.seed, verbose=True)
         else:
             res_df = []
             for gt_df, var_df, p_df, p_pos_df, _ in genotypeio.generate_paired_chunks(pgr, phenotype_df, phenotype_pos_df, args.chunk_size,
                                                                                       dosages=args.dosages, verbose=True):
-                res_df.append(cis.map_independent(gt_df, var_df, summary_df, p_df, p_pos_df, covariates_df,
+                res_df.append(cis.map_independent(gt_df, var_df, summary_df, p_df, p_pos_df, covariates_df=covariates_df,
                                                   group_s=group_s, fdr=args.fdr, nperm=args.permutations, window=args.window,
                                                   maf_threshold=maf_threshold, logger=logger, seed=args.seed, verbose=True))
             res_df = pd.concat(res_df).reset_index(drop=True)